
    ?Ki                         S SK Jr  S SKrS SKJr  S\R                  S\S\R                  4S jrS\R                  S	\R                  S
\R                  S\R                  S\\R                     S\4S jr	g)    )OptionalN)nnhidden_states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)
   N)shapeexpandreshape)r   r   batchnum_key_value_headsslenhead_dims         g/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/transformers/integrations/eager_paged.py	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTT    modulequerykeyvalueattention_maskscalingc                    UR                  SS 5      nUbb  UR                  " X#U R                  40 UD6u  p#UR                  SS5      R	                  S5      nUR                  SS5      R	                  S5      n[        U S5      (       a*  [        X R                  5      n[        X0R                  5      n[        U[        5      (       a  [        U SS5      nUS:X  d  Uc  SOSn	XI   n
OUn
[        R                  " XR                  SS	5      5      U-  nU
b  X-   n[        U S
5      (       a  U R                  R                  SSSS5      R                  UR                   S   SUR                   S   S5      n[        R"                  " X/SS9nXR%                  SSS9R&                  -
  n[(        R*                  R-                  US[        R.                  S9R1                  UR2                  5      nUSS S24   nOF[(        R*                  R-                  US[        R.                  S9R1                  UR2                  5      n[        R                  " X5      nUR                  SS5      R5                  5       nX4$ )Ncacher   r	   num_key_value_groupssliding_windowfull_attentionsliding_attention      sinks)dimT)r%   keepdim)r%   dtype.)popupdate	layer_idx	transpose	unsqueezehasattrr   r   
isinstancedictgetattrtorchmatmulr"   r   r   r
   catmaxvaluesr   
functionalsoftmaxfloat32tor'   
contiguous)r   r   r   r   r   r   kwargsr   r   
layer_typecausal_maskattn_weightsr"   attn_outputs                 r   eager_paged_attention_forwardr@      s,    JJw%E\\#f.>.>I&I
mmAq!++A.1%//2 v-..889%!<!<= .$'' )91=)71)<@V%\o
$0$<<}}Q':;gEL#1 vw$$QAq188QU[[Y[_^`ayy,!6B?#&6&62t&6&L&S&SS}},,\r,WZZ[`[f[fg#C"H-}},,\r,WZZ[`[f[fg,,|3K''1-88:K$$r   )
typingr   r1   r   Tensorintr   Modulefloatr@    r   r   <module>rG      s      	UU\\ 	U# 	U%,, 	U2%II2%<<2% 
2% <<	2%
 U\\*2% 2%r   