
    Z jL_                        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  SS
KJrJr  SSKJr  SSKJrJrJr  SSKJrJr  SSK J!r!J"r"  SSK#J$r$J%r%  SSK&J'r'  SSK(J)r)J*r*J+r+  SSK,J-r-J.r.  SSK/J0r0  SSK1J2r2   " S S\Rf                  5      r4 " S S\Rf                  5      r5 " S S\Rf                  5      r6S r7\" S5      S=S j5       r8S\Rr                  S \:S!\Rr                  4S" jr;   S>S#\Rf                  S$\Rr                  S%\Rr                  S&\Rr                  S'\Rr                  S-  S(\<\:-  S)\<S-  S*\<S-  S!\=\Rr                  \Rr                  4   4S+ jjr>\" \85       " S, S-\Rf                  5      5       r? " S. S/\5      r@ " S0 S1\R                  5      rB\* " S2 S3\%5      5       rC\* " S4 S5\C5      5       rD\* " S6 S7\C\5      5       rE " S8 S9\\C5      rF " S: S;\\C5      rG/ S<QrHg)?    )Callable)OptionalN   )initialization)ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_func_from_hubuse_kernelized_func)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )Gemma2Configc                   J   ^  \ rS rSrS	S\S\4U 4S jjjrS rS rS r	Sr
U =r$ )
Gemma2RMSNorm1   dimepsc                    > [         TU ]  5         X l        [        R                  " [
        R                  " U5      5      U l        g N)super__init__r&   nn	Parametertorchzerosweight)selfr%   r&   	__class__s      {/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/gemma2/modeling_gemma2.pyr*   Gemma2RMSNorm.__init__2   s,    ll5;;s#34    c                     U[         R                  " UR                  S5      R                  SSS9U R                  -   5      -  $ )N   T)keepdim)r-   rsqrtpowmeanr&   )r0   xs     r2   _normGemma2RMSNorm._norm7   s4    5;;quuQx}}R}>IJJJr4   c                     U R                  UR                  5       5      nUSU R                  R                  5       -   -  nUR                  U5      $ )N      ?)r=   floatr/   type_as)r0   r<   outputs      r2   forwardGemma2RMSNorm.forward:   sC    AGGI& 3!2!2!445~~a  r4   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler/   shaper&   )r0   s    r2   
extra_reprGemma2RMSNorm.extra_reprA   s'    ))*+6$((<<r4   )r&   r/   )gư>)__name__
__module____qualname____firstlineno__intrA   r*   r=   rD   rI   __static_attributes____classcell__r1   s   @r2   r#   r#   1   s0    5C 5e 5 5
K!= =r4   r#   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )	Gemma2MLPE   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFbias)r)   r*   confighidden_sizeintermediate_sizer+   Linear	gate_projup_proj	down_projr   hidden_activationact_fnr0   rZ   r1   s     r2   r*   Gemma2MLP.__init__F   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV556r4   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r(   )r`   rb   r^   r_   )r0   r<   r`   s      r2   rD   Gemma2MLP.forwardP   s6    NN4;;t~~a/@#ADLLQRO#ST	r4   )rb   rZ   r`   r^   r[   r\   r_   )rK   rL   rM   rN   r*   rD   rP   rQ   rR   s   @r2   rT   rT   E   s    7 r4   rT   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )Gemma2RotaryEmbeddingU   inv_freqNrZ   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_lenrZ   rope_parametersrl   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r0   rZ   devicerope_init_fnrj   r1   s        r2   r*   Gemma2RotaryEmbedding.__init__X   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr4   ry   ztorch.deviceseq_lenreturnztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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_dimNr@   r   r6   dtype)ry   r   )	rt   getattrr[   num_attention_headsr-   arangeint64torA   )rZ   ry   r|   baser%   attention_factorrj   s          r2   ru   5Gemma2RotaryEmbedding.compute_default_rope_parametersh   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r4   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r7   r    mpscpuF)device_typeenabledr6   r%   r   )rj   rA   expandrH   r   ry   
isinstancetypestrr   	transposer-   catcosrv   sinr   )
r0   r<   position_idsinv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r2   rD   Gemma2RotaryEmbedding.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#)rv   rZ   rr   rs   rl   r(   NNN)rK   rL   rM   rN   r-   Tensor__annotations__r!   r*   staticmethodr   rO   rG   rA   ru   no_gradr   rD   rP   rQ   rR   s   @r2   rh   rh   U   s    llV| V V  &*+/"*t#*(* t* 
~u$	%	* *: ]]_<  <r4   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$ )z*Rotates half the hidden dims of the input..Nr7   r6   r   )rH   r-   r   )r<   x1x2s      r2   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r4   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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          r2   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr4   hidden_states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)r   r   batchnum_key_value_headsslenr   s         r2   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr4   modulequerykeyvalueattention_maskdropoutscalingsoftcapc                 j   Uc  U R                   S-  n[        X R                  5      n	[        X0R                  5      n
[        R                  " XR                  SS5      5      U-  nUb  X-  n[        R                  " U5      nX-  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$ )N      r6   r   r7   )r%   r   )ptrainingr    )r   r   num_key_value_groupsr-   matmulr   tanhr+   
functionalsoftmaxfloat32r   r   r   r   
contiguous)r   r   r   r   r   r   r   r   kwargs
key_statesvalue_statesattn_weightsattn_outputs                r2   eager_attention_forwardr      s    //4'3 ; ;<JU$?$?@L<<';';Aq'ABWLL#-zz,/#-!#4 ==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r4   c                   0  ^  \ 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                  S-  \
\R                     S-  4   4S jjrSrU =r$ )Gemma2Attention   z=Multi-headed attention from 'Attention Is All You Need' paperrZ   	layer_idxc                 Z  > [         TU ]  5         [        US5      (       a  UR                  U   OS U l        Xl        X l        [        USUR                  UR                  -  5      U l
        UR                  UR                  -  U l        UR                  S-  U l        U R
                  R                  U l        [        USS5      (       + U l        ["        R$                  " 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
                  R0                  U l        U R                  S:X  a  UR2                  U l        g S U l        g )Nlayer_typesr   r   use_bidirectional_attentionFrX   sliding_attention)r)   r*   hasattrr   
layer_typerZ   r   r   r[   r   r   r   r   query_pre_attn_scalarr   attention_dropout	is_causalr+   r]   attention_biasq_projk_projv_projo_projattn_logit_softcappingsliding_windowr0   rZ   r   r1   s      r2   r*   Gemma2Attention.__init__   s   ;B6=;Y;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!33T9!%!>!>$V-JERRii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 '+kk&H&H#7;J]7]f33cgr4   Nr   position_embeddingsr   past_key_valuesr   r}   c                 4   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       a  U R                   OSU R"                  U R$                  U R&                  S.UD6u  pUR(                  " / UQSP76 R+                  5       nU R-                  U5      nX4$ )Nr7   r    r6           )r   r   r   r   )rH   r   r   viewr   r   r   r   updater   r   get_interfacerZ   _attn_implementationr   r   r   r   r   r   r   r   r   )r0   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r2   rD   Gemma2Attention.forward  s    $))#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$J(?(M(MKK,,.E)
 %8%
 /3mmD**LL..//%
 %
! "));;;;FFHkk+.((r4   )r   r   rZ   r   r   r   r   r   r   r   r   r   r   r   r   )rK   rL   rM   rN   __doc__r!   rO   r*   r-   r   rG   r   r   r   rD   rP   rQ   rR   s   @r2   r   r      s    Gh| h h: IM.2(,()||() #5<<#=>E() t+	()
 () -.() 
u||U\\D0%2E2LL	M() ()r4   r   c                   >  ^  \ rS rSrS\S\4U 4S jjr    SS\R                  S\	\R                  \R                  4   S-  S\R                  S-  S	\R                  S-  S
\S-  S\	\R                  \	\R                  \R                  4   S-  4   4S jjrSrU =r$ )Gemma2DecoderLayeri.  rZ   r   c                   > [         TU ]  5         UR                  U l        X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        [        UR                  UR                  S9U l        [        UR                  UR                  S9U l        g )N)rZ   r   r&   )r)   r*   r[   rZ   r   	self_attnrT   mlpr#   rms_norm_epsinput_layernormpost_attention_layernormpre_feedforward_layernormpost_feedforward_layernormr   s      r2   r*   Gemma2DecoderLayer.__init__/  s    !--(LV$,V-?-?VEXEXY(5f6H6HfNaNa(b%)6v7I7IvObOb)c&*78J8JPVPcPc*d'r4   Nr   r   r   r   r   r}   c           	          UnU R                  U5      nU R                  " SUUUUUS.UD6u  pU R                  U5      nXq-   nUnU R                  U5      nU R	                  U5      nU R                  U5      nXq-   nU$ )N)r   r   r   r   r    )r   r   r   r   r   r   )	r0   r   r   r   r   r   r   residual_s	            r2   rD   Gemma2DecoderLayer.forward;  s     !,,];  >> 
' 3)%+
 
 55mD 0 66}E/77F 0r4   )rZ   r[   r   r   r   r   r   r   )NNNN)rK   rL   rM   rN   r!   rO   r*   r-   r   rG   
LongTensorr   FloatTensorrD   rP   rQ   rR   s   @r2   r   r   .  s    
e| 
e 
e IM.204(,|| #5<<#=>E t+	
 &&-  
u  %(9(95;L;L(L"MPT"TT	U r4   r   c            	       l   ^  \ rS rSrSrSS\S\S\S\4U 4S jjjrS\R                  4U 4S	 jjr
S
rU =r$ )Gemma2TextScaledWordEmbeddingi]  zT
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
num_embeddingsembedding_dimpadding_idxembed_scalec                 |   > [         TU ]  XU5        X@l        U R                  S[        R
                  " U5      SS9  g )Nr  Frn   )r)   r*   scalar_embed_scalerw   r-   tensor)r0   r  r  r  r  r1   s        r2   r*   &Gemma2TextScaledWordEmbedding.__init__b  s7    D"-]ELL,ERWXr4   	input_idsc                    > [         TU ]  U5      U R                  R                  U R                  R
                  5      -  $ r(   )r)   rD   r  r   r/   r   )r0   r  r1   s     r2   rD   %Gemma2TextScaledWordEmbedding.forwardg  s2    wy)D,<,<,?,?@Q@Q,RRRr4   )r  )r@   )rK   rL   rM   rN   r   rO   rA   r*   r-   r   rD   rP   rQ   rR   s   @r2   r
  r
  ]  sM    Ys Y3 YS Y_d Y Y
S S Sr4   r
  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\R&                  " 5       U 4S j5       rS	rU =r$ )
Gemma2PreTrainedModelik  rZ   modelTr   r   )r   
attentionsc                   > [         TU ]  U5        SUR                  R                  ;   a!  [        R
                  " UR                  5        g [        U[        5      (       a,  [        R                  " UR                  UR                  5        g g )NRMSNorm)r)   _init_weightsr1   rK   initzeros_r/   r   r
  	constant_r  r  )r0   r   r1   s     r2   r  #Gemma2PreTrainedModel._init_weights}  sa    f%((111KK& =>>NN6--v/H/HI ?r4   r  )rK   rL   rM   rN   r!   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr-   r   r  rP   rQ   rR   s   @r2   r  r  k  sn    &*#-.#4"5N!"&+%
 ]]_J Jr4   r  c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )Gemma2Modeli  rZ   c           	      "  > [         TU ]  U5        UR                  U l        UR                  U l        [        UR                  UR                  U R                  U R                  R                  S-  S9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5      U l        SU l        U R+                  5         g s  snf )Ng      ?)r  r   F)r)   r*   pad_token_idr  
vocab_sizer
  r[   rZ   embed_tokensr+   
ModuleListrangenum_hidden_layersr   layersr#   r   normrh   
rotary_embgradient_checkpointing	post_initr   s      r2   r*   Gemma2Model.__init__  s     !.. ++9v1143C3CQUQ\Q\QhQhjmQm
 mmDI&JbJbDcdDcy2Dcd
 "&"4"4&:M:MN	/7&+# 	 es    DNr  r   r   r   inputs_embeds	use_cacher   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
0 U
D6[        S
0 U
D6S.n	UnU R                  X5      n[        U R                   S U R                  R"                   5       H,  u  pU" U4XR                  R$                  U      UUUS.UD6nM.     U R'                  U5      n[)        UUS	9$ )Nz:You must specify exactly one of input_ids or inputs_embeds)rZ   r   r    )ry   )rZ   r:  r   r   r   )full_attentionr   )r   r   r   r   )last_hidden_stater   r  )
ValueErrorr0  r	   rZ   get_seq_lengthr-   r   rH   ry   r   r   dictr   r   r6  	enumerater4  r3  r   r5  r   )r0   r  r   r   r   r:  r;  r   past_seen_tokenscausal_mask_mappingmask_kwargsr   r   idecoder_layers                  r2   rD   Gemma2Model.forward  s    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L ?-FF ++!."0#2 ,K #5"C{"C%F%U%U# &"oomJ )$++6U8U8U*V WA)2;;3J3J13MN$7) / M !X 		-0&++
 	
r4   )r0  r7  r4  r5  r  r6  r/  )NNNNNN)rK   rL   rM   rN   r!   r*   r   r   r   r-   r  r   r   r  boolr   r   r   rD   rP   rQ   rR   s   @r2   r,  r,    s    | $   .2.204(,26!%;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
 $;;
 +,;
 
!;
    ;
r4   r,  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )Gemma2ForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr   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 rW   )
r)   r*   r,  r  r/  r+   r]   r[   rL  r8  rc   s     r2   r*   Gemma2ForCausalLM.__init__  sU      (
 ++yy!3!3V5F5FUS 	r4   Nr  r   r   r   r:  labelsr;  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U R                  R                  bF  XR                  R                  -  n[        R                  " U5      nXR                  R                  -  nSnUb  U R                  " XU R                  40 U	D6n[        UUU
R                  U
R                  U
R                  S9$ )a"  
Example:

```python
>>> from transformers import AutoTokenizer, Gemma2ForCausalLM

>>> model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-9b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")

>>> prompt = "What is your favorite condiment?"
>>> 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]
"What is your favorite condiment?"
```)r  r   r   r   r:  r;  N)lossrN  r   r   r  r  )r  r>  r   rO   slicerL  rZ   final_logit_softcappingr-   r   loss_functionr/  r   r   r   r  )r0   r  r   r   r   r:  rQ  r;  rR  r   outputsr   slice_indicesrN  rT  s                  r2   rD   Gemma2ForCausalLM.forward  s   @ ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A;;..:kkAAAFZZ'FkkAAAF%%fdooPPD%#33!//))
 	
r4   )rL  r  r/  )NNNNNNNr   )rK   rL   rM   rN   _tied_weights_keys_tp_plan_pp_planr*   r   r   r-   r  r   r   r  rI  rO   r   r   r   rD   rP   rQ   rR   s   @r2   rK  rK    s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
 ;
  ;
r4   rK  c                       \ rS rSrSrg)Gemma2ForSequenceClassificationi+  r  NrK   rL   rM   rN   rP   r  r4   r2   r_  r_  +      r4   r_  c                       \ rS rSrSrg)Gemma2ForTokenClassificationi/  r  Nr`  r  r4   r2   rc  rc  /  ra  r4   rc  )rK  r,  r  r_  rc  )r    )r   NN)Icollections.abcr   typingr   r-   torch.nnr+    r   r  activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_gemma2r!   Moduler#   rT   rh   r   r   r   rO   r   rA   rG   r   r   r   	Embeddingr
  r  r,  rK  r_  rc  __all__r  r4   r2   <module>rz     s4  * %    & ! . ) I R B 
 P K F & I I G 5 .=BII =(		  ><BII ><B( *+ ,2	UU\\ 	U# 	U%,, 	U$   %II%<<% 
% <<	%
 LL4'% S[% T\% T\% 5<<%&%D )*E)bii E) +E)P,3 ,^SBLL S JO J J6 Q
' Q
 Q
h K
- K
 K
\	&FH] 		#@BW 	r4   