
    <hIR                        S SK JrJr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  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'  SSK(J)r)   " S S\RT                  5      r+\" S5       " S S\RT                  5      5       r, " S S\RT                  5      r-S r.S=S jr/S\R`                  S\1S\R`                  4S  jr2 S>S!\RT                  S"\R`                  S#\R`                  S$\R`                  S%\\R`                     S&\3S'\3S(\"\$   4S) jjr4 " S* S+\RT                  5      r5 " S, S-\5      r6\ " S. S/\ 5      5       r7\ " S0 S1\75      5       r8\" S2S39 " S4 S5\7\5      5       r9\" S2S39 " S6 S7\\75      5       r:\" S2S39 " S8 S9\\75      5       r;\" S2S39 " S: S;\\75      5       r</ S<Qr=g)?    )CallableOptionalUnionN)nn)auto_docstring   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargscan_return_tuple)check_model_inputs   )ArceeConfigc                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )ArceeMLP1   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     `/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/arcee/modeling_arcee.pyr&   ArceeMLP.__init__2   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      $ N)r-   r/   r,   )r1   xs     r3   forwardArceeMLP.forward;   s"    ~~dkk$,,q/:;;r5   )r/   r'   r-   r(   r)   r,   )__name__
__module____qualname____firstlineno__r&   r9   __static_attributes____classcell__r2   s   @r3   r    r    1   s    0< <r5   r    RMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )ArceeRMSNorm?   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
ArceeRMSNorm is equivalent to T5LayerNorm
N)r%   r&   r   	Parametertorchonesweightvariance_epsilon)r1   r(   epsr2   s      r3   r&   ArceeRMSNorm.__init__A   s/     	ll5::k#:; #r5   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetorH   float32powmeanrsqrtrK   rJ   )r1   hidden_statesinput_dtypevariances       r3   r9   ArceeRMSNorm.forwardI   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r5   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tuplerJ   shaperK   r1   s    r3   
extra_reprArceeRMSNorm.extra_reprP   s*    ))*+6$2G2G1HIIr5   )rK   rJ   )gư>)	r;   r<   r=   r>   r&   r9   r`   r?   r@   rA   s   @r3   rD   rD   ?   s    $;J Jr5   rD   c                   l   ^  \ rS rSrSS\4U 4S jjjr\R                  " 5       \S 5       5       r	Sr
U =r$ )ArceeRotaryEmbeddingT   r'   c                   > [         TU ]  5         [        US5      (       aZ  [        UR                  [
        5      (       a;  UR                  R                  SUR                  R                  S5      5      U l        OSU l        UR                  U l	        UR                  U l
        Xl        [        U R                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                   U l        g )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r%   r&   hasattr
isinstancerf   dictgetrg   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr'   r   rope_init_fnattention_scalingregister_bufferrj   original_inv_freq)r1   r'   devicerj   r2   s       r3   r&   ArceeRotaryEmbedding.__init__U   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r5   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   rP   r   mpscpuF)device_typeenabledrO   dim)rR   )rj   floatexpandr^   rS   rw   rm   rh   strrH   autocast	transposecatcosrt   sinrR   )
r1   r8   position_idsinv_freq_expandedposition_ids_expandedr|   freqsembr   r   s
             r3   r9   ArceeRotaryEmbedding.forwardf   sR    !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   $BF  
F.)rt   r'   rq   rv   rr   rs   rg   r7   )r;   r<   r=   r>   r   r&   rH   no_gradr   r9   r?   r@   rA   s   @r3   rc   rc   T   s6    /{ / /" ]]_<  <r5   rc   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..NrP   rO   r~   )r^   rH   r   )r8   x1x2s      r3   rotate_halfr   v   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r5   c                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )a  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.
    position_ids (`torch.Tensor`, *optional*):
        Deprecated and unused.
    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   r   unsqueeze_dimq_embedk_embeds           r3   apply_rotary_pos_embr   }   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr5   rX   n_repreturnc                     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)r^   r   reshape)rX   r   batchnum_key_value_headsslenhead_dims         r3   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr5   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"  US S 2S S 2S S 2S UR
                  S   24   n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$ )NrO   r   rP   )r   rR   )ptrainingr   )r   num_key_value_groupsrH   matmulr   r^   r   
functionalsoftmaxrT   rS   rR   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r3   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r5   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
\\R                     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   getattrr(   num_attention_headsr   r   r   r   attention_dropout	is_causalr   r*   attention_biasq_projk_projv_projo_projr1   r'   r   r2   s      r3   r&   ArceeAttention.__init__   sI   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r5   rX   position_embeddingsr   past_key_valuecache_positionr   r   c                 4   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nUu  p[        XX5      u  pUb$  XUS.nUR                  XU R                  U5      u  p[        nU R                  R                  S:w  a  [        U R                  R                     nU" U U	U
UU4U R                  (       d  SOU R                  U R                   S.UD6u  nnUR"                  " / UQSP76 R%                  5       nU R'                  U5      nUU4$ )NrP   r   rO   )r   r   r   eager        )r   r   )r^   r   r   viewr   r   r   r   updater   r   r'   _attn_implementationr   r   r   r   r   r   r   )r1   rX   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   cache_kwargsattention_interfacer   r   s                     r3   r9   ArceeAttention.forward   s    $))#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#[ %#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHkk+.L((r5   )r   r'   r   r   r   r   r   r   r   r   r   )NN)r;   r<   r=   r>   __doc__r   intr&   rH   Tensorr]   r   r
   
LongTensorr   r   r9   r?   r@   rA   s   @r3   r   r      s    G
{ 
s 
8 +/59))||)) #5<<#=>)) !.	))
 !)) !!1!12)) +,)) 
u||U\\)	*)) ))r5   r   c                   8  ^  \ rS rSrS\S\4U 4S jjr      SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\R                     S\	\\R                  \R                  4      S\\   S\\R                     4S jjrSrU =r$ )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   rL   )r%   r&   r(   r   	self_attnr    mlprD   rms_norm_epsinput_layernormpost_attention_layernormr   s      r3   r&   ArceeDecoderLayer.__init__  sj    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r5   rX   r   r   r   	use_cacher   r   r   r   c                     Un	U R                  U5      nU R                  " SU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$ )N)rX   r   r   r   r   r   r    )r   r   r   r   )r1   rX   r   r   r   r   r   r   r   residual_s              r3   r9   ArceeDecoderLayer.forward  s     !,,];>> 	
')%)) 3	
 	
 !0 !55mD/ 0r5   )r(   r   r   r   r   )NNNFNN)r;   r<   r=   r>   r   r   r&   rH   r   r   r   r
   boolr]   r   r   r9   r?   r@   rA   s   @r3   r   r     s    b{ bs b 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	 r5   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	)
ArceePreTrainedModeli1  r'   modelTr   past_key_values)rX   
attentionsr   N)r;   r<   r=   r>   r   __annotations__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_outputsr?   r   r5   r3   r   r   1  sQ    &*#,-#4"5N!"&*$r5   r   c                     ^  \ rS rSrS\4U 4S jjr\\       SS\\	R                     S\\	R                     S\\	R                     S\\   S\\	R                     S	\\	R                     S
\\   S\\   S\4S jj5       5       rSrU =r$ )
ArceeModeliD  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   layersrD   r   normrc   
rotary_embgradient_checkpointing	post_initr   s      r3   r&   ArceeModel.__init__F  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+# 	 ds   C>	input_idsr   r   r   inputs_embedsr   r   r   r   c           
      8   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc
  [        5       nUcD  Ub  UR                  5       OSn	[        R
                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U R                  UUUUUS9n
UnU R                  X5      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   )rw   )r'   input_embedsr   r   r   r   )r   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r   get_seq_lengthrH   aranger^   rw   r   r   r'   r  r
  r	  r  r   )r1   r  r   r   r   r  r   r   r   past_seen_tokensr   rX   r   decoder_layers                 r3   r9   ArceeModel.forwardV  sK    -t";<YZZ *.*;*;I*FM0*nO!CRC^==?de+0<< ]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oomJ![[)H4;;+H+HIM)*).-$7 M J 		-0&++
 	
r5   )r  r  r
  r  r  r  r  )NNNNNNN)r;   r<   r=   r>   r   r&   r   r   r   rH   r   r   r
   FloatTensorr   r   r   r   r9   r?   r@   rA   s   @r3   r   r   D  s    {    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r5   r   zarcee-ai/AFM-4.5B)
checkpointc                   ~  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jrS rS	 r	\
\         SS
\\R                     S\\R                     S\\R                     S\\   S\\R"                     S\\R                     S\\   S\\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )ArceeForCausalLMi  zlm_head.weightlm_headcolwise_reprX   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g )NFr#   )
r%   r&   r   r   r  r   r*   r(   r  r  r0   s     r3   r&   ArceeForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r5   c                     Xl         g r7   r   )r1   decoders     r3   set_decoderArceeForCausalLM.set_decoder  s    
r5   c                     U R                   $ r7   r%  r_   s    r3   get_decoderArceeForCausalLM.get_decoder  s    zzr5   r  r   r   r   r  labelsr   r   logits_to_keepr   r   c
                 ~   U R                   " SU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   r   N)r!  r,  r  )lossr!  r   rX   r   r   )r   r  rm   r   slicer  loss_functionr'   r  r   r   rX   r   )r1   r  r   r   r   r  r,  r   r   r-  r   outputsrX   slice_indicesr!  r/  s                   r3   r9   ArceeForCausalLM.forward  s    @ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r5   )r  r   r  )	NNNNNNNNr   )r;   r<   r=   r>   _tied_weights_keys_tp_plan_pp_planr&   r'  r*  r   r   r   rH   r   r   r
   r  r   r   r   r   r   r   r9   r?   r@   rA   s   @r3   r  r    s:   *+=)H_-z:;H  151537+/59-1$(59348
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 ))*8
 D>8
 !!1!128
 c5<</08
 +,8
 
 8
  8
r5   r  c                       \ rS rSrSrg)ArceeForSequenceClassificationi  r   Nr;   r<   r=   r>   r?   r   r5   r3   r9  r9        r5   r9  c                       \ rS rSrSrSrg)ArceeForQuestionAnsweringi  transformerr   N)r;   r<   r=   r>   r   r?   r   r5   r3   r=  r=    s    %r5   r=  c                       \ rS rSrSrg)ArceeForTokenClassificationi  r   Nr:  r   r5   r3   r@  r@    r;  r5   r@  )r  r=  r9  r@  r   r   )Nr   )r   )>typingr   r   r   rH   r   transformers.utilsr   activationsr	   cache_utilsr
   r   
generationr   integrationsr   masking_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.genericr   configuration_arceer   Moduler    rD   rc   r   r   r   r   r   r   r   r   r   r   r   r  r9  r=  r@  __all__r   r5   r3   <module>rR     s.  , - ,   - ! . ) 7 /  P K F & 9 / ,<ryy < Y'J299 J (J(<299 <D(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4C)RYY C)L*2 *Z ?  $ K
% K
 K
\ ./N
+_ N
 0N
b ./	%EG[ 	 0	 ./& ;=Q & 0& ./	"?AU 	 0	r5   