
    <hj                        S SK JrJrJr  S SKrS SKJs  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Jr  SS
K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#  SSK$J%r%J&r&J'r'  SSK(J)r)  SSK*J+r+  \" S5       " S S\RX                  5      5       r- " S S\RX                  5      r.S r/S9S jr0S\Rb                  S\2S\Rb                  4S jr3 S:S\RX                  S \Rb                  S!\Rb                  S"\Rb                  S#\\Rb                     S$\4S%\4S&\#\%   4S' jjr5 " S( S)\RX                  5      r6 " S* S+\RX                  5      r7 " S, S-\RX                  5      r8 " S. S/\RX                  5      r9 " S0 S1\5      r:\& " S2 S3\!5      5       r;\& " S4 S5\;5      5       r<\& " S6 S7\;\5      5       r=/ S8Qr>g);    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)check_model_inputs   )Dots1ConfigRMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )Dots1RMSNorm+   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
Dots1RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      `/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/dots1/modeling_dots1.pyr$   Dots1RMSNorm.__init__-   s/     	ll5::k#:; #    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      -  $ )N   T)keepdim)	dtypetor&   float32powmeanrsqrtr)   r(   )r*   hidden_statesinput_dtypevariances       r.   forwardDots1RMSNorm.forward5   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r0   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler(   shaper)   r*   s    r.   
extra_reprDots1RMSNorm.extra_repr<   s*    ))*+6$2G2G1HIIr0   )r)   r(   )gư>)	__name__
__module____qualname____firstlineno__r$   r>   rD   __static_attributes____classcell__r-   s   @r.   r    r    +   s    $;J Jr0   r    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$ )Dots1RotaryEmbedding@   configc                   > [         TU ]  5         [        US5      (       aZ  [        UR                  [
        5      (       a;  UR                  R                  SUR                  R                  S5      5      U l        OSU l        UR                  U l	        UR                  U l
        Xl        [        U R                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                   U l        g )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r#   r$   hasattr
isinstancerR   dictgetrS   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrP   r   rope_init_fnattention_scalingregister_bufferrV   original_inv_freq)r*   rP   devicerV   r-   s       r.   r$   Dots1RotaryEmbedding.__init__A   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r0   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r3   r   mpscpuF)device_typeenabledr2   dimr5   )rV   floatexpandrB   r6   rc   rY   rT   strr&   autocast	transposecatcosr`   sinr5   )
r*   xposition_idsinv_freq_expandedposition_ids_expandedrh   freqsembrs   rt   s
             r.   r>   Dots1RotaryEmbedding.forwardR   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   $BF  
F.)r`   rP   r]   rb   r^   r_   rS   N)rF   rG   rH   rI   r   r$   r&   no_gradr   r>   rJ   rK   rL   s   @r.   rN   rN   @   s6    /{ / /" ]]_<  <r0   rN   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$ )z*Rotates half the hidden dims of the input..Nr3   r2   rj   )rB   r&   rr   )ru   x1x2s      r.   rotate_halfr   b   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r0   c                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )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krs   rt   rv   unsqueeze_dimq_embedk_embeds           r.   apply_rotary_pos_embr   i   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr0   r;   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)rB   rn   reshape)r;   r   batchnum_key_value_headsslenhead_dims         r.   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr0   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$ )Nr2   r   r3   )rk   r5   )ptrainingr   )r   num_key_value_groupsr&   matmulrq   rB   r   
functionalsoftmaxr7   r6   r5   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r.   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r0   c                   F  ^  \ 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                     \\
\R                        4   4S jjrSrU =r$ )Dots1Attention   z=Multi-headed attention from 'Attention Is All You Need' paperrP   	layer_idxc                 4  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        SU l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR                  U R                  -  UR
                  UR                  S9U l        [)        U R                  UR*                  S9U l        [)        U R                  UR*                  S9U l        UR0                  U   S:X  a  UR2                  U l        g S U l        g )Nr   g      Tbiasr,   sliding_attention)r#   r$   rP   r   getattrr+   num_attention_headsr   r   r   r   attention_dropout	is_causalr   Linearattention_biasq_projk_projv_projo_projr    rms_norm_epsq_normk_normlayer_typessliding_windowr*   rP   r   r-   s      r.   r$   Dots1Attention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 #4==f6I6IJ"4==f6I6IJ7=7I7I)7TXk7kf33qur0   r;   position_embeddingsr   past_key_valuecache_positionr   r   c                    UR                   S S n/ UQSPU R                  P7nU R                  U R                  U5      R	                  U5      5      R                  SS5      n	U R                  U R                  U5      R	                  U5      5      R                  SS5      n
U R                  U5      R	                  U5      R                  SS5      nUu  p[        XX5      u  pUb$  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$                  U R&                  S.UD6u  nnUR(                  " / UQSP76 R+                  5       nU R-                  U5      nUU4$ )Nr3   r   r2   )rt   rs   r   eager        )r   r   r   )rB   r   r   r   viewrq   r   r   r   r   updater   r   rP   _attn_implementationr   r   r   r   r   r   r   r   )r*   r;   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rs   rt   cache_kwargsattention_interfacer   r   s                     r.   r>   Dots1Attention.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#[ %#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL..
%
 
%
!\ "));;;;FFHkk+.L((r0   )r   rP   r   r   r   r   r   r   r   r   r   r   r   r   NN)rF   rG   rH   rI   __doc__r   intr$   r&   TensorrA   r   r	   
LongTensorr   r   r>   rJ   rK   rL   s   @r.   r   r      s    Gv{ vs v> +/59*)||*) #5<<#=>*) !.	*)
 !*) !!1!12*) -.*) 
u||Xell3XeELL>Q5RR	S*) *)r0   r   c                   2   ^  \ rS rSrSU 4S jjrS rSrU =r$ )Dots1MLP   c                   > [         TU ]  5         Xl        Uc  UR                  OUU l        Uc  UR                  OUU l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFr   )r#   r$   rP   r+   intermediate_sizer   r   	gate_projup_proj	down_projr   
hidden_actact_fn)r*   rP   r+   r   r-   s       r.   r$   Dots1MLP.__init__   s    1<1D6--+=N=V!9!9\m4#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r0   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r|   )r   r   r   r   )r*   ru   r   s      r.   r>   Dots1MLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r0   )r   rP   r   r   r+   r   r   r   )rF   rG   rH   rI   r$   r>   rJ   rK   rL   s   @r.   r   r      s    	0 r0   r   c                      ^  \ rS rSrSrU 4S jrS\R                  S\R                  S\R                  4S jrS r	S	r
U =r$ )
Dots1MoEi  z2
A mixed expert module containing shared experts.
c           
      @  > [         TU ]  5         Xl        [        R                  " [        UR                  5       Vs/ sH  n[        XR                  S9PM     sn5      U l	        [        U5      U l        [        XR                  UR                  -  S9U l        g s  snf )N)r   )rP   r   )r#   r$   rP   r   
ModuleListrangen_routed_expertsr   moe_intermediate_sizeexpertsDots1TopkRoutergaten_shared_expertsshared_experts)r*   rP   _r-   s      r.   r$   Dots1MoE.__init__
  s    }}W\]c]t]tWuvWuRSXf0L0LMWuv
 $F+	&-I-IFLcLc-c
 ws   Br;   topk_indicestopk_weightsc                 J   [         R                  " XR                  S9n[         R                  R                  R                  U[        U R                  5      S9nUR                  SSS5      n[        [        U R                  5      5       H{  nU R                  U   nXV   n[         R                  " U5      u  pU	R                  5       S:  d  MD  X9U
4   nX   nU" U5      nXR                  S5      -  nUR                  SX5        M}     UR                  UR                  5      $ )z
CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused
to not have to do a loop here (deepseek has 256 experts soooo yeah).
rl   )num_classesr2   r   r   r3   )r&   
zeros_liker5   r   r   one_hotlenr   permuter   wherenumelr   
index_add_rT   )r*   r;   r   r   final_hidden_statesexpert_mask
expert_idxexpertmasktoken_indicesweight_indicesexpert_weightsexpert_inputexpert_outputweighted_outputs                  r.   moeDots1MoE.moe  s   
 $..}DVDVWhh))11,CPTP\P\L]1^!))!Q2DLL 12J\\*-F*D,1KK,=)M""$q(!-^.K!L,; &| 4"/2J2J22N"N#..q-Q 3 #''(;(;<<r0   c                     UnUR                   nU R                  U5      u  pEUR                  SUR                   S   5      nU R                  XU5      R                  " U6 nXR	                  U5      -   nU$ )Nr3   )rB   r   r   r
  r   )r*   r;   	residuals
orig_shaper   r   s         r.   r>   Dots1MoE.forward/  su    !	"((
%)YY}%="%**2}/B/B2/FGlKPPR\]%(;(;I(FFr0   )rP   r   r   r   )rF   rG   rH   rI   r   r$   r&   r   r
  r>   rJ   rK   rL   s   @r.   r   r     s@    	
= =U\\ =Y^YeYe =4 r0   r   c                   \   ^  \ rS rSrU 4S jr\R                  " 5       S 5       rS rSr	U =r
$ )r   i9  c                   > [         TU ]  5         Xl        UR                  U l        UR
                  U l        UR                  U l        UR                  U l        UR                  U l        UR                  U l	        [        R                  " [        R                  " U R
                  UR                  45      5      U l        U R!                  S[        R"                  " U R
                  5      5        g )Ne_score_correction_bias)r#   r$   rP   num_experts_per_toktop_kr   routed_scaling_factorn_group
topk_groupnorm_topk_probr   r%   r&   emptyr+   r(   ra   zerosr*   rP   r-   s     r.   r$   Dots1TopkRouter.__init__:  s    //
 & 7 7%+%A%A"~~ ++$33ll5;;0E0EvGYGY/Z#[\6DDYDY8Z[r0   c                    UR                  SU R                  5      U R                  R                  S5      -   nUR                  SU R                  U R                  U R                  -  5      R                  SSS9S   R                  SS9n[        R
                  " X0R                  SSS9S   n[        R                  " U5      nUR                  SUS5        UR                  S5      R                  SU R                  U R                  U R                  -  5      R                  SU R                  5      nUR                  UR                  5       ) S5      n[        R
                  " X R                  SSS9S   nU$ )	Nr3   r   r2   rj   F)r   rk   sortedr   r   )r   r   r  r   r  topksumr&   r  r   scatter_rn   r   masked_fillboolr  )r*   scoresscores_for_choicegroup_scores	group_idx
group_mask
score_maskr   s           r.   get_topk_indices Dots1TopkRouter.get_topk_indicesG  sB   "KKD,A,ABTEaEaEkEklmEnn""2t||T5J5Jdll5Z[T!T_Q SRS[ 	
 JJ|BuUVWX	%%l3
Ay!,  $VBd&;&;t||&KLWR../ 	
 .99:??;L:LcRzz"3zzrRWXYZ[r0   c                    UR                  SU R                  R                  5      n[        R                  " UR                  [        R                  5      U R                  R                  [        R                  5      5      nUR                  5       nU R                  U5      nUR                  SU5      nU R                  (       a  UR                  SSS9S-   nXV-  nXPR                  -  nXE4$ )Nr3   r   T)rk   r4   g#B;)r   rP   r+   FlinearrT   r&   r7   r(   sigmoidr*  gatherr  r   r  )r*   r;   router_logitsr$  r   r   denominators          r.   r>   Dots1TopkRouter.forward[  s    %**2t{{/F/FG!3!3EMM!BDKKDTDTUZUbUbDcd&&(,,V4}}Q5&**r4*@5HK'L#&@&@@))r0   )rP   r  r   r  r  r  r  r(   )rF   rG   rH   rI   r$   r&   r}   r*  r>   rJ   rK   rL   s   @r.   r   r   9  s-    \ ]]_ &
* 
*r0   r   c                   8  ^  \ rS rSrS\S\4U 4S jjr      SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\R                     S\	\\R                  \R                  4      S\\   S\\R                     4S jjrSrU =r$ )Dots1DecoderLayerih  rP   r   c                 t  > [         TU ]  5         UR                  U l        [        XS9U l        X!R
                  :  a  [        U5      U l        O[        U5      U l        [        UR                  UR                  S9U l        [        UR                  UR                  S9U l        UR                  U   U l        g )N)rP   r   r   )r#   r$   r+   r   	self_attnfirst_k_dense_replacer   mlpr   r    r   input_layernormpost_attention_layernormr   attention_typer   s      r.   r$   Dots1DecoderLayer.__init__i  s    !--'vK444'DH'DH+F,>,>FDWDWX(4V5G5GVM`M`(a%$00;r0   r;   r   rv   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$ )N)r;   r   rv   r   r>  r   r    )r:  r7  r;  r9  )r*   r;   r   rv   r   r>  r   r   r   residualr   s              r.   r>   Dots1DecoderLayer.forwardx  s     !,,];>> 	
')%)) 3	
 	
 !0 !55mD/ 0r0   )r<  r+   r:  r9  r;  r7  )NNNFNN)rF   rG   rH   rI   r   r   r$   r&   r   r   r   r	   r#  rA   r   r   r>   rJ   rK   rL   s   @r.   r5  r5  h  s    <{ <s <$ 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	 r0   r5  c                   f   ^  \ 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U 4S jrS	rU =r$ )
Dots1PreTrainedModeli  rP   modelTr5  past_key_values)r;   
attentionsc                    > [         TU ]  U5        [        U[        5      (       a9  UR                  R
                  R                  SU R                  R                  S9  g g )Nr   )r9   std)	r#   _init_weightsrY   r   r(   datanormal_rP   initializer_range)r*   r   r-   s     r.   rJ  "Dots1PreTrainedModel._init_weights  sI    f%fo..MM&&CT[[5R5R&S /r0   r@  )rF   rG   rH   rI   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr5  r   _can_record_outputsrJ  rJ   rK   rL   s   @r.   rD  rD    s^    &*#,-#4"5N!"&*$
T Tr0   rD  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	\\   S
\\	R                     S\\   S\4S jj5       5       rSrU =r$ )
Dots1Modeli  rP   c           	      B  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ sH  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        SU R(                  R*                  ;   U l        U R/                  5         g s  snf )Nr   )rP   Fr   )r#   r$   pad_token_idpadding_idx
vocab_sizer   	Embeddingr+   embed_tokensr   r   num_hidden_layersr5  layersr    r   normrN   
rotary_embgradient_checkpointingrP   r   has_sliding_layers	post_initr   s      r.   r$   Dots1Model.__init__  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+#"59P9P"P 	 ds   D	input_idsr   rv   rF  inputs_embedsr>  r   r   r   c                    US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc
  [        5       nUcD  Ub  UR                  5       OSn	[        R
                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U=n
[        5      (       d?  U R                  UUUUUS.nS[        S
0 UD60n
U R                  (       a  [        S
0 UD6U
S'   UnU R                  X5      nU R                   S U R                  R"                    H  nU" U4XR$                     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   )rc   )rP   input_embedsr   r   rF  rv   full_attentionr   )r   rv   r   r>  r   r   )last_hidden_staterF  r@  )
ValueErrorra  r
   get_seq_lengthr&   arangerB   rc   r   rY   rZ   rP   r   rg  r   re  rc  rb  r<  rd  r   )r*   rj  r   rv   rF  rk  r>  r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr;   r   decoder_layers                  r.   r>   Dots1Model.forward  s    -t";<YZZ  --i8M0*nO!CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L ?-FF ++ -"0"0#2 ,K !"4"C{"C# &&;\;k_j;k#$78% #oomJ![[)H4;;+H+HIM)	23O3OP).#-$7	 	M J 		-0&+/8O
 	
>B
 	
r0   )ra  rf  rg  rc  rd  r^  re  r_  )NNNNNNN)rF   rG   rH   rI   r   r$   r   r   r   r&   r   r   r	   FloatTensorr#  r   r   r   r>   rJ   rK   rL   s   @r.   r[  r[    s    { "  151537+/59$(59E
E,,-E
 !.E
 u//0	E

 "%E
   1 12E
 D>E
 !!1!12E
 +,E
 
!E
  E
r0   r[  c                   ~  ^  \ 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$ )Dots1ForCausalLMi  zlm_head.weightlm_headcolwise_repr;   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g r   )
r#   r$   r[  rE  r_  r   r   r+   r{  rh  r  s     r.   r$   Dots1ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r0   c                     Xl         g r|   rE  )r*   decoders     r.   set_decoderDots1ForCausalLM.set_decoder  s    
r0   c                     U R                   $ r|   r  rC   s    r.   get_decoderDots1ForCausalLM.get_decoder!  s    zzr0   rj  r   rv   rF  rk  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$ )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, Dots1ForCausalLM

>>> model = Dots1ForCausalLM.from_pretrained("rednote-hilab/dots1.llm1.inst")
>>> tokenizer = AutoTokenizer.from_pretrained("rednote-hilab/dots1.llm1.inst")

>>> 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."
```)rj  r   rv   rF  rk  r>  r   N)r}  r  r_  )lossr}  rF  r;   rG  r@  )rE  ro  rY   r   slicer{  loss_functionrP   r_  r   rF  r;   rG  )r*   rj  r   rv   rF  rk  r  r>  r   r  r   outputsr;   slice_indicesr}  r  s                   r.   r>   Dots1ForCausalLM.forward$  s    J ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r0   )r{  rE  r_  )	NNNNNNNNr   )rF   rG   rH   rI   _tied_weights_keys_tp_plan_pp_planr$   r  r  r   r   r   r&   r   r   r	   rx  r#  r   r   r   r   r   r>   rJ   rK   rL   s   @r.   rz  rz    s:   *+=)H_-z:;H  151537+/59-1$(5934=
E,,-=
 !.=
 u//0	=

 "%=
   1 12=
 ))*=
 D>=
 !!1!12=
 c5<</0=
 +,=
 
 =
  =
r0   rz  )rD  r[  rz  )Nr   )r   )?typingr   r   r   r&   torch.nn.functionalr   r   r-  activationsr   cache_utilsr	   r
   
generationr   integrationsr   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   configuration_dots1r   Moduler    rN   r   r   r   r   r   rm   r   r   r   r   r   r5  rD  r[  rz  __all__r@  r0   r.   <module>r     s  * - ,     ! . ) 7 R B 9 O K F & I I / , Y'J299 J (J(<299 <D(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4G)RYY G)Tryy "1ryy 1h,*bii ,*^/2 /d T? T T. Y
% Y
 Y
x S
+_ S
 S
l Er0   