
    Z j p                        S SK Jr  S SK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  SSKJrJr  SS	KJr  SS
KJrJrJrJr  SSKJrJr  SSKJr  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/J0r0  SSK1J2r2  SSK3J4r4  \" S5       " S S\Rj                  5      5       r6 " S S\Rj                  5      r7S r8\" S5      S?S j5       r9S\Rt                  S \;S!\Rt                  4S" jr< S@S#\Rj                  S$\Rt                  S%\Rt                  S&\Rt                  S'\Rt                  S-  S(\=S)\=S*\)\+   4S+ jjr>\" \95       " S, S-\Rj                  5      5       r? " S. S/\Rj                  5      r@ " S0 S1\Rj                  5      rA\ " S2 S3\Rj                  5      5       rB " S4 S5\Rj                  5      rC " S6 S7\5      rD\, " S8 S9\'5      5       rE\, " S: S;\E5      5       rF\, " S< S=\E\5      5       rG/ S>QrHg)A    )Callable)OptionalN)nn   )initialization)ACT2FN)CacheDynamicCache)GenerationMixin)use_experts_implementationuse_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)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)maybe_autocastmerge_with_config_defaults)capture_outputs   )Dots1Config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$ )Dots1RMSNorm2   epsreturnNc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
Dots1RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizer'   	__class__s      y/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/dots1/modeling_dots1.pyr+   Dots1RMSNorm.__init__4   s/     	ll5::k#:; #    hidden_statesc                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetor-   float32powmeanrsqrtr0   r/   )r1   r7   input_dtypevariances       r4   forwardDots1RMSNorm.forward<   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r6   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler/   shaper0   )r1   s    r4   
extra_reprDots1RMSNorm.extra_reprC   s*    ))*+6$2G2G1HIIr6   )r0   r/   )gư>)__name__
__module____qualname____firstlineno__floatr+   r-   TensorrD   rI   __static_attributes____classcell__r3   s   @r4   r%   r%   2   sB    $ $$ $ $;U\\ ;ell ;J Jr6   r%   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$ )Dots1RotaryEmbeddingG   inv_freqNconfigc                   > [         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defaultrW   F)
persistentoriginal_inv_freq)r*   r+   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrX   rope_parametersrZ   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r1   rX   devicerope_init_fnrW   r3   s        r4   r+   Dots1RotaryEmbedding.__init__J   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr6   rf   ztorch.deviceseq_lenr(   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	aH  
Computes the inverse frequencies according to the original RoPE implementation
Args:
    config ([`~transformers.PreTrainedConfig`]):
        The model configuration.
    device (`torch.device`):
        The device to use for initialization of the inverse frequencies.
    seq_len (`int`, *optional*):
        The current sequence length. Unused for this type of RoPE.
Returns:
    Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
    post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).

rope_thetahead_dimNg      ?r   r9   r<   )rf   r<   )	ra   getattrr2   num_attention_headsr-   arangeint64r=   rO   )rX   rf   ri   basedimattention_factorrW   s          r4   rb   4Dots1RotaryEmbedding.compute_default_rope_parametersZ   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r6   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r:   r!   mpscpuF)device_typeenabledr9   rs   rm   )rW   rO   expandrH   r=   rf   
isinstancetypestrr   	transposer-   catcosrc   sinr<   )
r1   xposition_idsinv_freq_expandedposition_ids_expandedry   freqsembr   r   s
             r4   rD   Dots1RotaryEmbedding.forwardx   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#)rc   rX   r_   r`   rZ   N)NNN)rK   rL   rM   rN   r-   rP   __annotations__r"   r+   staticmethodr   intrG   rO   rb   no_gradr   rD   rQ   rR   rS   s   @r4   rU   rU   G   s    llV{ V V  %)+/"*d"*(* t* 
~u$	%	* *: ]]_<  <r6   rU   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..Nr:   r9   r{   )rH   r-   r   )r   x1x2s      r4   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r6   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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          r4   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr6   r7   n_repr(   c                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r!   N)rH   r|   reshape)r7   r   batchnum_key_value_headsslenrl   s         r4   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr6   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$ )Nr9   r   r:   )rs   r<   )ptrainingr!   )r   num_key_value_groupsr-   matmulr   r   
functionalsoftmaxr>   r=   r<   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r4   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r6   c                     ^  \ rS rSrSrS\S\4U 4S jjr SS\R                  S\
\R                  \R                  4   S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  S-  4   4S jjrSrU =r$ )Dots1Attention   z=Multi-headed attention from 'Attention Is All You Need' paperrX   	layer_idxc                 |  > [         TU ]  5         [        US5      (       a  UR                  U   OS U l        Xl        X l        [        USUR                  UR                  -  5      U l
        UR                  UR                  -  U l        U R                  S-  U l        UR                  U l        SU l        [         R"                  " UR                  UR                  U R                  -  UR$                  S9U l        [         R"                  " UR                  UR                  U R                  -  UR$                  S9U l        [         R"                  " UR                  UR                  U R                  -  UR$                  S9U l        [         R"                  " UR                  U R                  -  UR                  UR$                  S9U l        [/        U R                  UR0                  S9U l        [/        U R                  UR0                  S9U l        U R                  S:X  a  UR6                  U l        g S U l        g )Nlayer_typesrl   g      Tbiasr'   sliding_attention)r*   r+   hasattrr   
layer_typerX   r   rn   r2   ro   rl   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sliding_windowr1   rX   r   r3   s      r4   r+   Dots1Attention.__init__   s   ;B6=;Y;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 #4==f6I6IJ"4==f6I6IJ7;J]7]f33cgr6   Nr7   position_embeddingsr   past_key_valuesr   r(   c                 Z   UR                   S S n/ UQSPU R                  P7nU R                  U R                  U5      R	                  U5      5      R                  SS5      nU R                  U R                  U5      R	                  U5      5      R                  SS5      n	U R                  U5      R	                  U5      R                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [         5      nU" U UU	U
U4U R"                  (       d  SOU R$                  U R&                  U R(                  S.UD6u  pUR*                  " / UQSP76 R-                  5       nU R/                  U5      nX4$ )Nr:   r!   r9           )r   r   r   )rH   rl   r   r   viewr   r   r   r   r   updater   r   get_interfacerX   _attn_implementationr   r   r   r   r   r   r   r   )r1   r7   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r4   rD   Dots1Attention.forward   s    $))#2.88b8$--8{{4;;}#=#B#B<#PQ[[\]_`a[[]!;!@!@!NOYYZ[]^_
{{=166|DNNqRST&#7RU#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
! "));;;;FFHkk+.((r6   )r   rX   rl   r   r   r   r   r   r   r   r   r   r   r   r   r   )rK   rL   rM   rN   __doc__r"   r   r+   r-   rP   rG   r	   r   r   rD   rQ   rR   rS   s   @r4   r   r      s    Gh{ hs h@ )-')||') #5<<#=>') t+	')
 ') -.') 
u||U\\D00	1') ')r6   r   c                   2   ^  \ rS rSrSU 4S jjrS rSrU =r$ )Dots1MLPi  c                   > [         TU ]  5         Xl        UR                  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+   rX   r2   intermediate_sizer   r   	gate_projup_proj	down_projr   
hidden_actact_fn)r1   rX   r   r3   s      r4   r+   Dots1MLP.__init__  s    !--=N=V!9!9\m4#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r6   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r   )r   r   r   r   )r1   r   r   s      r4   rD   Dots1MLP.forward"  s6    NN4;;t~~a/@#ADLLQRO#ST	r6   )r   rX   r   r   r2   r   r   r   rK   rL   rM   rN   r+   rD   rQ   rR   rS   s   @r4   r   r     s    0 r6   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )Dots1TopkRouteri'  c                 :  > [         TU ]  5         X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+   rX   n_routed_expertsr   r,   r-   emptyr2   r/   rd   zerosr1   rX   r3   s     r4   r+   Dots1TopkRouter.__init__(  sk     & 7 7ll5;;0E0EvGYGY/Z#[\6DDYDY8Z[r6   c                    UR                  SU R                  R                  5      n[        R                  " UR                  [        R                  5      U R                  R                  [        R                  5      5      nU$ Nr:   )	r   rX   r2   Flinearr~   r-   r>   r/   )r1   r7   router_logitss      r4   rD   Dots1TopkRouter.forward0  sY    %**2t{{/F/FG!3!3EMM!BDKKDTDTUZUbUbDcdr6   )rX   r   r/   r   rS   s   @r4   r   r   '  s    \ r6   r   c                      ^  \ rS rSrSrU 4S jrS\R                  S\R                  S\R                  S\R                  4S jrS	r	U =r
$ )
Dots1NaiveMoei6  z2Collection of expert weights stored as 3D tensors.c                   > [         TU ]  5         UR                  U l        UR                  U l        UR                  U l        [        R                  " [        R                  " U R                  SU R                  -  U R
                  5      5      U l        [        R                  " [        R                  " U R                  U R
                  U R                  5      5      U l        [        UR                     U l        g )Nr9   )r*   r+   num_local_expertsnum_expertsr2   
hidden_dimmoe_intermediate_sizeintermediate_dimr   r,   r-   r   gate_up_projr   r   r   r   r   s     r4   r+   Dots1NaiveMoe.__init__:  s    !33 ,, & < <LLT5E5Eq4K`K`G`bfbqbq)rsekk$2B2BDOOUYUjUj&klV../r6   r7   top_k_indextop_k_weightsr(   c                 X   [         R                  " U5      n[         R                  " 5          [         R                  R                  R                  X R                  S9nUR                  SSS5      n[         R                  " UR                  SS9S5      R                  5       nS S S 5        W H  nUS   nXpR                  :X  a  M  [         R                  " WU   5      u  pX   n
[        R                  R                  XR                  U   5      R                  SSS9u  pU R                  U5      U-  n[        R                  R                  XR                   U   5      nXXS 4   -  nUR#                  SXR%                  UR&                  5      5        M     U$ ! , (       d  f       N= f)N)num_classesr9   r!   r   )r:   r{   r:   )r-   
zeros_liker   r   r   one_hotr  permutegreatersumnonzerowherer   r  chunkr   r   
index_add_r=   r<   )r1   r7   r  r	  final_hidden_statesexpert_mask
expert_hit
expert_idx	top_k_pos	token_idxcurrent_stategateupcurrent_hidden_statess                 r4   rD   Dots1NaiveMoe.forwardC  so    $..}=]]_((--55kO_O_5`K%--aA6K{8'DaHPPRJ 
 %J#AJ---#(;;{:/F#G I)4M}}++M;L;LZ;XY__`agi_jHD$(KK$5$:!$&MM$8$89NP^P^_iPj$k!$9)`dJd<e$e!**1i9Q9QReRkRk9lm % #"# _s   A7F
F))r   r   r  r  r  r  )rK   rL   rM   rN   r   r+   r-   rP   rD   rQ   rR   rS   s   @r4   r   r   6  sK    <0#||# \\# ||	#
 
# #r6   r   c                   8   ^  \ rS rSrSrU 4S jrS rS rSrU =r	$ )Dots1MoEi^  z2
A mixed expert module containing shared experts.
c                   > [         TU ]  5         Xl        [        U5      U l        [        U5      U l        [        XR                  UR                  -  S9U l
        UR                  U l        UR                  U l        UR                  U l        UR                  U l        UR                  U l        UR                   U l        g )N)rX   r   )r*   r+   rX   r   expertsr   r  r   r  n_shared_expertsshared_expertsr   n_group
topk_groupnorm_topk_probrouted_scaling_factornum_experts_per_toktop_kr   s     r4   r+   Dots1MoE.__init__c  s    $V,#F+	&-I-IFLcLc-c
 !' 7 7~~ ++$33%+%A%A"//
r6   c                 x   UR                  5       nXR                  R                  -   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      5      n[        R                  " XpR$                  SSS9S   nUR'                  SU5      n	U R(                  (       a  U	R                  SS	S
9S-   n
X-  n	XR*                  -  n	X4$ )Nr:   r9   r{   r   F)r   rs   sortedr!   z-infT)rs   r;   g#B;)sigmoidr  r   r   r'  r   topkr  r-   r(  r  scatter_r   r|   r   masked_fillboolrO   r,  gatherr)  r*  )r1   r   router_logits_for_choicegroup_scores	group_idx
group_mask
score_maskscores_for_choicetopk_indicestopk_weightsdenominators              r4   route_tokens_to_experts Dots1MoE.route_tokens_to_expertsr  s   %--/#0993T3T#T $))"dllD<Q<QUYUaUa<abT!T_Q SRS[ 	
 JJ|BuUVWX	%%l3
Ay!,  $VBd&;&;t||&KLWR../ 	
 5@@*//BSASUZ[aUbczz"3zzrRWXYZ[$++A|<&**r4*@5HK'L#&@&@@))r6   c                    UnUR                   nU R                  U5      nU R                  U5      u  pVUR                  SUR                   S   5      nU R	                  XU5      R                  " U6 nXR                  U5      -   nU$ r   )rH   r  r?  r   r$  r&  )r1   r7   	residuals
orig_shaper   r<  r=  s          r4   rD   Dots1MoE.forward  s    !	"((
		-0%)%A%A-%P"%**2}/B/B2/FG],OTTV`a%(;(;I(FFr6   )
rX   r$  r  r'  r   r)  r*  r&  r,  r(  )
rK   rL   rM   rN   r   r+   r?  rD   rQ   rR   rS   s   @r4   r"  r"  ^  s    0*2 r6   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$ )Dots1DecoderLayeri  rX   r   c                 L  > [         TU ]  5         UR                  U l        [        XS9U l        X!R
                  :  a  [        U5      U l        O[        U5      U l        [        UR                  UR                  S9U l        [        UR                  UR                  S9U l        g )N)rX   r   r   )r*   r+   r2   r   	self_attnfirst_k_dense_replacer"  mlpr   r%   r   input_layernormpost_attention_layernormr   s      r4   r+   Dots1DecoderLayer.__init__  s    !--'vK444'DH'DH+F,>,>FDWDWX(4V5G5GVM`M`(a%r6   Nr7   r   r   r   	use_cacher   r   r(   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r7   r   r   r   rN  r    )rK  rH  rL  rJ  )
r1   r7   r   r   r   rN  r   r   residual_s
             r4   rD   Dots1DecoderLayer.forward  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r6   )r2   rK  rJ  rL  rH  )NNNFN)rK   rL   rM   rN   r"   r   r+   r-   rP   
LongTensorr	   r4  rG   r   r   rD   rQ   rR   rS   s   @r4   rF  rF    s    b{ bs b" /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r6   rF  c                      ^  \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rS/rS	r\R*                  " 5       U 4S
 j5       rSrU =r$ )Dots1PreTrainedModeli  rX   modelTrF  r   )r7   
attentionsr   Nc                   > [         TU ]  U5        [        U[        5      (       aU  [        R
                  " UR                  SU R                  R                  S9  [        R                  " UR                  5        g [        U[        5      (       ai  [        R
                  " UR                  SU R                  R                  S9  [        R
                  " UR                  SU R                  R                  S9  g g )Nr   )r@   std)r*   _init_weightsr}   r   initnormal_r/   rX   initializer_rangezeros_r   r   r  r   )r1   r   r3   s     r4   r[  "Dots1PreTrainedModel._init_weights  s    f%fo..LLSdkk6S6STKK667..LL,,3DKK<Y<YZLL))9V9VW /r6   rP  )rK   rL   rM   rN   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_backendrF  r   _can_record_outputs_keep_in_fp32_modules_strict"_keys_to_ignore_on_load_unexpectedr-   r   r[  rQ   rR   rS   s   @r4   rV  rV    s}    &*#,-#4"5N!"&*$ %>#> )-&
]]_X Xr6   rV  c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )
Dots1Modeli  rX   c           	      D  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        SU R(                  R*                  ;   U l        U R/                  5         g s  snf )Nr   rX   Fr   )r*   r+   pad_token_idpadding_idx
vocab_sizer   	Embeddingr2   embed_tokens
ModuleListrangenum_hidden_layersrF  layersr%   r   normrU   
rotary_embgradient_checkpointingrX   r   has_sliding_layers	post_initr   s      r4   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N	input_idsr   r   r   inputs_embedsrN  r   r(   c           
         US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U=n	[        5      (       d>  U R                  UUUUS.n
S[        S0 U
D60n	U R                  (       a  [        S0 U
D6U	S'   UnU R                  X5      n[!        U R"                  S U R                  R$                   5       H-  u  pU" U4XR                  R&                  U      UUUUS	.UD6nM/     U R)                  U5      n[+        UU(       a  US
9$ S S
9$ )Nz:You must specify exactly one of input_ids or inputs_embedsrp  r   r!   )rf   )rX   r  r   r   r   full_attentionr   )r   r   r   r   rN  )last_hidden_stater   rP  )
ValueErrorru  r
   rX   get_seq_lengthr-   rp   rH   rf   r   r}   dictr   r}  r   r{  	enumeratery  rx  r   rz  r   )r1   r  r   r   r   r  rN  r   past_seen_tokenscausal_mask_mappingmask_kwargsr7   r   idecoder_layers                  r4   rD   Dots1Model.forward  s    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L ?-FF ++!."0#2 ,K !"4"C{"C# &&;\;k_j;k#$78%"oomJ )$++6U8U8U*V WA)2;;3J3J13MN$7) /# M !X 		-0&+/8O
 	
>B
 	
r6   )ru  r|  r}  ry  rz  rr  r{  rs  )NNNNNN)rK   rL   rM   rN   r"   r+   r   r    r   r-   rT  rP   r	   FloatTensorr4  r   r   r   rD   rQ   rR   rS   s   @r4   rn  rn    s    { "   .2.204(,26!%<
##d*<
 t+<
 &&-	<

 <
 ((4/<
 $;<
 +,<
 
!<
    <
r6   rn  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )Dots1ForCausalLMi9  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr7   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+   rn  rW  rs  r   r   r2   r  r~  r   s     r4   r+   Dots1ForCausalLM.__init__?  sU     '
 ++yy!3!3V5F5FUS 	r6   Nr  r   r   r   r  labelsrN  logits_to_keepr   r(   c	           
      |   U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U	D6n[        UUU
R                  U
R                  U
R                  S9$ )a  
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
    config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
    (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

Example:

```python
>>> from transformers import AutoTokenizer, 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."
```)r  r   r   r   r  rN  N)r  r  rs  )lossr  r   r7   rX  rP  )rW  r  r}   r   slicer  loss_functionrX   rs  r   r   r7   rX  )r1   r  r   r   r   r  r  rN  r  r   outputsr7   slice_indicesr  r  s                  r4   rD   Dots1ForCausalLM.forwardH  s    H ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r6   )r  rW  rs  )NNNNNNNr   )rK   rL   rM   rN   _tied_weights_keys_tp_plan_pp_planr+   r   r   r-   rT  rP   r	   r  r4  r   r   r   r   rD   rQ   rR   rS   s   @r4   r  r  9  s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
 ;
  ;
r6   r  )rV  rn  r  )r!   )r   )Icollections.abcr   typingr   r-   torch.nn.functionalr   r   r    r   r\  activationsr   cache_utilsr	   r
   
generationr   integrationsr   r   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr    configuration_dots1r"   Moduler%   rU   r   r   rP   r   r   rO   r   r   r   r   r   r"  rF  rV  rn  r  __all__rP  r6   r4   <module>r     s8  ( %      & ! . )  S B 9 O K F & I I G 5 , Y'J299 J (J(><299 ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*E)RYY E) +E)Pryy  bii  $#BII $# $#N5ryy 5p,2 ,^ X? X X< Q
% Q
 Q
h K
+_ K
 K
\ Er6   