
    hn`              	          d Z ddlZddlZddlmZ ddlmZmZ ddlZddl	m
c mZ ddlm
Z
 ddlmZ ddlmZmZ dd	lmZ dd
lmZmZ ddlmZmZ ddlmZ  ej8                  e      Zd,dej>                  de de!dej>                  fdZ" G d de
jF                        Z$ G d de
jF                        Z% G d de
jF                        Z& G d de
jF                        Z' G d de
jF                        Z( G d de
jF                        Z) G d d e
jF                        Z* G d! d"e
jF                        Z+e G d# d$e             Z,e G d% d&e,             Z- ed'(       G d) d*e,             Z.g d+Z/y)-zPyTorch PVT model.    N)Iterable)OptionalUnion)nn   )ACT2FN)BaseModelOutputImageClassifierOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer)auto_docstringlogging   )	PvtConfiginput	drop_probtrainingreturnc                    |dk(  s|s| S d|z
  }| j                   d   fd| j                  dz
  z  z   }|t        j                  || j                  | j
                        z   }|j                          | j                  |      |z  }|S )aF  
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
            r   r   )r   )dtypedevice)shapendimtorchrandr   r   floor_div)r   r   r   	keep_probr   random_tensoroutputs          c/var/www/html/aiagenthome/venv/lib/python3.12/site-packages/transformers/models/pvt/modeling_pvt.py	drop_pathr$   (   s     CxII[[^

Q 77E

5ELL YYMYYy!M1FM    c                   x     e Zd ZdZd	dee   ddf fdZdej                  dej                  fdZ	de
fdZ xZS )
PvtDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr   r   c                 0    t         |           || _        y N)super__init__r   )selfr   	__class__s     r#   r+   zPvtDropPath.__init__@   s    "r%   hidden_statesc                 D    t        || j                  | j                        S r)   )r$   r   r   r,   r.   s     r#   forwardzPvtDropPath.forwardD   s    FFr%   c                      d| j                    S )Nzp=)r   )r,   s    r#   
extra_reprzPvtDropPath.extra_reprG   s    DNN#$$r%   r)   )__name__
__module____qualname____doc__r   floatr+   r   Tensorr1   strr3   __classcell__r-   s   @r#   r'   r'   =   sG    b#(5/ #T #GU\\ Gell G%C %r%   r'   c                        e Zd ZdZ	 ddedeeee   f   deeee   f   dedededef fd	Z	d
e
j                  dedede
j                  fdZde
j                  dee
j                  eef   fdZ xZS )PvtPatchEmbeddingsz
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    config
image_size
patch_sizestridenum_channelshidden_size	cls_tokenc                    t         	|           || _        t        |t        j
                  j                        r|n||f}t        |t        j
                  j                        r|n||f}|d   |d   z  |d   |d   z  z  }|| _        || _        || _	        || _
        t        j                  t        j                  d|r|dz   n||            | _        |r*t        j                  t        j                   dd|            nd | _        t        j$                  ||||      | _        t        j(                  ||j*                        | _        t        j.                  |j0                        | _        y )Nr   r   kernel_sizerB   eps)p)r*   r+   r?   
isinstancecollectionsabcr   r@   rA   rC   num_patchesr   	Parameterr   randnposition_embeddingszerosrE   Conv2d
projection	LayerNormlayer_norm_eps
layer_normDropouthidden_dropout_probdropout)
r,   r?   r@   rA   rB   rC   rD   rE   rO   r-   s
            r#   r+   zPvtPatchEmbeddings.__init__R   s0    	#-j+//:R:R#SZZdfpYq
#-j+//:R:R#SZZdfpYq
!!}
15*Q-:VW=:XY$$(&#%<<KKi;?[+V$
  JSekk!Q&DEX\))L+6Zde,,{8M8MNzzF$>$>?r%   
embeddingsheightwidthr   c                    ||z  }t         j                  j                         s<|| j                  j                  | j                  j                  z  k(  r| j
                  S |j                  d||d      j                  dddd      }t        j                  |||fd      }|j                  dd||z        j                  ddd      }|S )Nr   r   r      bilinear)sizemode)
r   jit
is_tracingr?   r@   rR   reshapepermuteFinterpolate)r,   r\   r]   r^   rO   interpolated_embeddingss         r#   interpolate_pos_encodingz+PvtPatchEmbeddings.interpolate_pos_encodingn   s    un yy##%+9O9ORVR]R]RhRh9h*h+++''65"=EEaAqQ
"#--
&%Wa"b"9"A"A!RRW"X"`"`abdegh"i&&r%   pixel_valuesc                    |j                   \  }}}}|| j                  k7  rt        d      | j                  |      }|j                   ^ }}}|j	                  d      j                  dd      }| j                  |      }| j                  | j                  j                  |dd      }	t        j                  |	|fd      }| j                  | j                  d d dd f   ||      }
t        j                  | j                  d d d df   |
fd      }
n| j                  | j                  ||      }
| j                  ||
z         }|||fS )NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.ra   r   r`   dim)r   rC   
ValueErrorrU   flatten	transposerX   rE   expandr   catrl   rR   r[   )r,   rm   
batch_sizerC   r]   r^   patch_embed_r\   rE   rR   s              r#   r1   zPvtPatchEmbeddings.forwardy   sM   2>2D2D/
L&%4,,,w  ool3'--FE!))!,66q!<__[1
>>%--j"bAIIz#:BJ"&"?"?@X@XYZ\]\^Y^@_agin"o"'))T-E-Ea!e-LNa,bhi"j"&"?"?@X@XZ`bg"h\\*/B"BC
65((r%   F)r4   r5   r6   r7   r   r   intr   boolr+   r   r9   rl   tupler1   r;   r<   s   @r#   r>   r>   K   s      @@ #x},-@ #x},-	@
 @ @ @ @8	'5<< 	' 	'UX 	']b]i]i 	')ELL )U5<<c;Q5R )r%   r>   c                   `     e Zd Zdedef fdZdej                  dej                  fdZ xZ	S )PvtSelfOutputr?   rD   c                     t         |           t        j                  ||      | _        t        j
                  |j                        | _        y r)   )r*   r+   r   LineardenserY   rZ   r[   )r,   r?   rD   r-   s      r#   r+   zPvtSelfOutput.__init__   s6    YY{K8
zz&"<"<=r%   r.   r   c                 J    | j                  |      }| j                  |      }|S r)   )r   r[   r0   s     r#   r1   zPvtSelfOutput.forward   s$    

=1]3r%   )
r4   r5   r6   r   rz   r+   r   r9   r1   r;   r<   s   @r#   r~   r~      s1    >y >s >
U\\ ell r%   r~   c                        e Zd ZdZdedededef fdZdedej                  fd	Z
	 ddej                  d
edededeej                     f
dZ xZS )PvtEfficientSelfAttentionzxEfficient self-attention mechanism with reduction of the sequence [PvT paper](https://huggingface.co/papers/2102.12122).r?   rD   num_attention_headssequences_reduction_ratioc                    t         |           || _        || _        | j                  | j                  z  dk7  r&t	        d| j                   d| j                   d      t        | j                  | j                  z        | _        | j                  | j                  z  | _        t        j                  | j                  | j                  |j                        | _        t        j                  | j                  | j                  |j                        | _        t        j                  | j                  | j                  |j                        | _        t        j                  |j                        | _        || _        |dkD  rEt        j$                  ||||      | _        t        j(                  ||j*                        | _        y y )	Nr   zThe hidden size (z6) is not a multiple of the number of attention heads ())biasr   rG   rI   )r*   r+   rD   r   rq   rz   attention_head_sizeall_head_sizer   r   qkv_biasquerykeyvaluerY   attention_probs_dropout_probr[   r   rT   sequence_reductionrV   rW   rX   r,   r?   rD   r   r   r-   s        r#   r+   z"PvtEfficientSelfAttention.__init__   sr    	&#6 d666!;#D$4$4#5 622316 
 $'t'7'7$:R:R'R#S !558P8PPYYt//1C1C&//Z
99T--t/A/AXYYt//1C1C&//Z
zz&"E"EF)B&$q(&(ii[6OXq'D# !ll;F<Q<QRDO	 )r%   r.   r   c                     |j                         d d | j                  | j                  fz   }|j                  |      }|j	                  dddd      S )Nr`   r   ra   r   r   )rc   r   r   viewrh   )r,   r.   	new_shapes      r#   transpose_for_scoresz.PvtEfficientSelfAttention.transpose_for_scores   sT    !&&("-1I1I4KcKc0dd	%**95$$Q1a00r%   r]   r^   output_attentionsc                    | j                  | j                  |            }| j                  dkD  r{|j                  \  }}}|j	                  ddd      j                  ||||      }| j                  |      }|j                  ||d      j	                  ddd      }| j                  |      }| j                  | j                  |            }	| j                  | j                  |            }
t        j                  ||	j                  dd            }|t        j                  | j                        z  }t         j"                  j%                  |d      }| j'                  |      }t        j                  ||
      }|j	                  dddd      j)                         }|j+                         d d | j,                  fz   }|j/                  |      }|r||f}|S |f}|S )Nr   r   ra   r`   ro   r   )r   r   r   r   rh   rg   r   rX   r   r   r   matmulrs   mathsqrtr   r   
functionalsoftmaxr[   
contiguousrc   r   r   )r,   r.   r]   r^   r   query_layerrv   seq_lenrC   	key_layervalue_layerattention_scoresattention_probscontext_layernew_context_layer_shapeoutputss                   r#   r1   z!PvtEfficientSelfAttention.forward   s    //

=0IJ))A-0=0C0C-J)11!Q:BB:|]cejkM 33MBM)11*lBOWWXY[\^_`M OOM:M--dhh}.EF	//

=0IJ !<<Y5H5HR5PQ+dii8P8P.QQ --//0@b/I ,,7_kB%--aAq9DDF"/"4"4"6s";t?Q?Q>S"S%**+BC6G=/2 O\M]r%   ry   )r4   r5   r6   r7   r   rz   r8   r+   r   r9   r   r{   r|   r1   r;   r<   s   @r#   r   r      s     CSS.1SHKShmS:1# 1%,, 1 #(*||* * 	*
  * 
u||	*r%   r   c                        e Zd Zdedededef fdZd Z	 ddej                  ded	ed
e
deej                     f
dZ xZS )PvtAttentionr?   rD   r   r   c                     t         |           t        ||||      | _        t	        ||      | _        t               | _        y )N)rD   r   r   )rD   )r*   r+   r   r,   r~   r"   setpruned_headsr   s        r#   r+   zPvtAttention.__init__   sB     	-# 3&?	
	 $FDEr%   c                 >   t        |      dk(  ry t        || j                  j                  | j                  j                  | j
                        \  }}t        | j                  j                  |      | j                  _        t        | j                  j                  |      | j                  _        t        | j                  j                  |      | j                  _	        t        | j                  j                  |d      | j                  _        | j                  j                  t        |      z
  | j                  _        | j                  j                  | j                  j                  z  | j                  _        | j
                  j                  |      | _        y )Nr   r   ro   )lenr   r,   r   r   r   r   r   r   r   r"   r   r   union)r,   headsindexs      r#   prune_headszPvtAttention.prune_heads   s   u:?749900$))2O2OQUQbQb
u
 -TYY__eD		*499==%@		,TYY__eD		.t{{/@/@%QO )-		(E(EE
(R		%"&))"?"?$))B_B_"_		 --33E:r%   r.   r]   r^   r   r   c                 h    | j                  ||||      }| j                  |d         }|f|dd  z   }|S )Nr   r   )r,   r"   )r,   r.   r]   r^   r   self_outputsattention_outputr   s           r#   r1   zPvtAttention.forward  sE     yy?PQ;;|A7#%QR(88r%   ry   )r4   r5   r6   r   rz   r8   r+   r   r   r9   r{   r|   r1   r;   r<   s   @r#   r   r      sn    "".1"HK"hm";& _d"\\36?BW[	u||	r%   r   c            
       z     e Zd Z	 	 d	dededee   dee   f fdZdej                  dej                  fdZ	 xZ
S )
PvtFFNr?   in_featureshidden_featuresout_featuresc                 j   t         |           ||n|}t        j                  ||      | _        t        |j                  t              rt        |j                     | _	        n|j                  | _	        t        j                  ||      | _
        t        j                  |j                        | _        y r)   )r*   r+   r   r   dense1rL   
hidden_actr:   r   intermediate_act_fndense2rY   rZ   r[   )r,   r?   r   r   r   r-   s        r#   r+   zPvtFFN.__init__  s     	'3'?|[ii_=f''-'-f.?.?'@D$'-'8'8D$ii>zz&"<"<=r%   r.   r   c                     | j                  |      }| j                  |      }| j                  |      }| j                  |      }| j                  |      }|S r)   )r   r   r[   r   r0   s     r#   r1   zPvtFFN.forward)  sP    M200?]3M2]3r%   )NN)r4   r5   r6   r   rz   r   r+   r   r9   r1   r;   r<   s   @r#   r   r     sY    
 *.&*>> > "#	>
 sm>"U\\ ell r%   r   c                   f     e Zd Zdedededededef fdZddej                  d	ed
ede	fdZ
 xZS )PvtLayerr?   rD   r   r$   r   	mlp_ratioc                 v   t         |           t        j                  ||j                        | _        t        ||||      | _        |dkD  rt        |      nt        j                         | _
        t        j                  ||j                        | _        t        ||z        }t        |||      | _        y )NrI   )r?   rD   r   r   r   )r?   r   r   )r*   r+   r   rV   rW   layer_norm_1r   	attentionr'   Identityr$   layer_norm_2rz   r   mlp)	r,   r?   rD   r   r$   r   r   mlp_hidden_sizer-   s	           r#   r+   zPvtLayer.__init__3  s     	LL&:O:OP%# 3&?	
 4=s?Y/LL&:O:OPkI56[Rabr%   r.   r]   r^   r   c                    | j                  | j                  |      |||      }|d   }|dd  }| j                  |      }||z   }| j                  | j	                  |            }| j                  |      }||z   }	|	f|z   }|S )N)r.   r]   r^   r   r   r   )r   r   r$   r   r   )
r,   r.   r]   r^   r   self_attention_outputsr   r   
mlp_outputlayer_outputs
             r#   r1   zPvtLayer.forwardI  s    !%++M:/	 "0 "
 2!4(,>>*:;(=8XXd//>?
^^J/
$z1/G+r%   ry   )r4   r5   r6   r   rz   r8   r+   r   r9   r{   r1   r;   r<   s   @r#   r   r   2  so    cc c !	c
 c $)c c,U\\ 3 s _c r%   r   c                   x     e Zd Zdef fdZ	 	 	 d	dej                  dee   dee   dee   de	e
ef   f
dZ xZS )

PvtEncoderr?   c                    t         	|           || _        t        j                  d|j
                  t        |j                        d      j                         }g }t        |j                        D ]  }|j                  t        ||dk(  r|j                  n| j                  j                  d|dz   z  z  |j                  |   |j                  |   |dk(  r|j                   n|j"                  |dz
     |j"                  |   ||j                  dz
  k(                t%        j&                  |      | _        g }d}t        |j                        D ]  }g }|dk7  r||j                  |dz
     z  }t        |j                  |         D ]\  }|j                  t+        ||j"                  |   |j,                  |   |||z      |j.                  |   |j0                  |                ^ |j                  t%        j&                  |              t%        j&                  |      | _        t%        j4                  |j"                  d   |j6                  	      | _        y )
Nr   cpu)r   ra   r   )r?   r@   rA   rB   rC   rD   rE   )r?   rD   r   r$   r   r   r`   rI   )r*   r+   r?   r   linspacedrop_path_ratesumdepthstolistrangenum_encoder_blocksappendr>   r@   patch_sizesstridesrC   hidden_sizesr   
ModuleListpatch_embeddingsr   r   sequence_reduction_ratios
mlp_ratiosblockrV   rW   rX   )
r,   r?   drop_path_decaysr\   iblockscurlayersjr-   s
            r#   r+   zPvtEncoder.__init__a  s    !>>!V-B-BCDV_delln 
v001A"!45Fv00@V@V[\abefaf[g@h%11!4!>>!,89Q!4!4FDWDWXY\]X]D^ & 3 3A 66#<#<q#@@
 2 !#j 9 v001AFAvv}}QU++6==+,%$*$7$7$:,2,F,Fq,I"237";282R2RST2U"("3"3A"6	 - MM"--/0! 2$ ]]6*
 ,,v':':2'>FDYDYZr%   rm   r   output_hidden_statesreturn_dictr   c                 2   |rdnd }|rdnd }|j                   d   }t        | j                        }|}	t        t	        | j
                  | j                              D ]|  \  }
\  }} ||	      \  }	}}|D ]&  } ||	|||      }|d   }	|r	||d   fz   }|s!||	fz   }( |
|dz
  k7  sI|	j                  |||d      j                  dddd      j                         }	~ | j                  |	      }	|r||	fz   }|st        d |	||fD              S t        |	||      S )	N r   r   r`   r   ra   c              3   &   K   | ]	  }||  y wr)   r   ).0vs     r#   	<genexpr>z%PvtEncoder.forward.<locals>.<genexpr>  s     m$[q_`_l$[s   last_hidden_stater.   
attentions)r   r   r   	enumeratezipr   rg   rh   r   rX   r|   r	   )r,   rm   r   r   r   all_hidden_statesall_self_attentionsrv   
num_blocksr.   idxembedding_layerblock_layerr]   r^   r   layer_outputss                    r#   r1   zPvtEncoder.forward  s\    #7BD$5b4!''*
_
$3<SAVAVX\XbXb=c3d/C//;+:=+I(M65$ %mVUDU V -a 0$*=qAQ@S*S''(9]<L(L% % j1n$ - 5 5j&%QS T \ \]^`acdfg h s s u 4e 6 1]4D Dm]4EGZ$[mmm++*
 	
r%   )FFT)r4   r5   r6   r   r+   r   FloatTensorr   r{   r   r|   r	   r1   r;   r<   s   @r#   r   r   `  sn    0[y 0[j -2/4&*#
''#
 $D>#
 'tn	#

 d^#
 
uo%	&#
r%   r   c                   H    e Zd ZU eed<   dZdZg Zdej                  ddfdZ
y)PvtPreTrainedModelr?   pvtrm   moduler   Nc                 l   | j                   j                  }t        |t        j                  t        j
                  f      rht        j                  j                  |j                  j                  d|       |j                  %|j                  j                  j                          yyt        |t        j                        rJ|j                  j                  j                          |j                  j                  j                  d       yt        |t              rt        j                  j                  |j                  j                  d|      |j                  _	        |j                   Ft        j                  j                  |j                   j                  d|      |j                   _	        yyy)zInitialize the weightsr   )meanstdNg      ?)r?   initializer_rangerL   r   r   rT   inittrunc_normal_weightdatar   zero_rV   fill_r>   rR   rE   )r,   r  r  s      r#   _init_weightsz PvtPreTrainedModel._init_weights  s@   kk++fryy"))45 GG!!&--"4"43C!H{{&  &&( '-KK""$MM$$S) 23.0gg.C.C**// /D /F&&+
 +(*(=(=$$)) )> )  % , 4r%   )r4   r5   r6   r   __annotations__base_model_prefixmain_input_name_no_split_modulesr   Moduler  r   r%   r#   r   r     s0    $OBII $ r%   r   c                        e Zd Zdef fdZd Ze	 	 	 d
dej                  de	e
   de	e
   de	e
   deeef   f
d	       Z xZS )PvtModelr?   c                 r    t         |   |       || _        t        |      | _        | j                          y r)   )r*   r+   r?   r   encoder	post_initr,   r?   r-   s     r#   r+   zPvtModel.__init__  s1      "&) 	r%   c                     |j                         D ]7  \  }}| j                  j                  |   j                  j	                  |       9 y)z
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        N)itemsr  layerr   r   )r,   heads_to_pruner  r   s       r#   _prune_headszPvtModel._prune_heads  s>    
 +002LE5LLu%//;;EB 3r%   rm   r   r   r   r   c                 ,   ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }| j	                  ||||      }|d   }|s	|f|dd  z   S t        ||j                  |j                        S )Nrm   r   r   r   r   r   r   )r?   r   r   use_return_dictr  r	   r.   r   )r,   rm   r   r   r   encoder_outputssequence_outputs          r#   r1   zPvtModel.forward  s     2C1N-TXT_T_TqTq$8$D $++JjJj 	 &1%<k$++B]B],,%/!5#	 ' 
 *!,#%(;;;-)77&11
 	
r%   )NNN)r4   r5   r6   r   r+   r  r   r   r   r   r{   r   r|   r	   r1   r;   r<   s   @r#   r  r    s    y C  -1/3&*
''
 $D>
 'tn	

 d^
 
uo%	&
 
r%   r  z
    Pvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.
    )custom_introc                        e Zd Zdeddf fdZe	 	 	 	 ddeej                     deej                     dee	   dee	   d	ee	   de
eef   fd
       Z xZS )PvtForImageClassificationr?   r   Nc                 0   t         |   |       |j                  | _        t        |      | _        |j                  dkD  r-t        j                  |j                  d   |j                        nt        j                         | _	        | j                          y )Nr   r`   )r*   r+   
num_labelsr  r  r   r   r   r   
classifierr  r  s     r#   r+   z"PvtForImageClassification.__init__  sy      ++F# FLEVEVYZEZBIIf))"-v/@/@A`b`k`k`m 	
 	r%   rm   labelsr   r   r   c                 R   ||n| j                   j                  }| j                  ||||      }|d   }| j                  |dddddf         }d}	|| j	                  ||| j                         }	|s|f|dd z   }
|	|	f|
z   S |
S t        |	||j                  |j                        S )a  
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        Nr  r   r   )losslogitsr.   r   )r?   r   r  r(  loss_functionr
   r.   r   )r,   rm   r)  r   r   r   r   r"  r,  r+  r"   s              r#   r1   z!PvtForImageClassification.forward#  s     &1%<k$++B]B]((%/!5#	  
 "!*Aq!9:%%ffdkkBDY,F)-)9TGf$EvE$!//))	
 	
r%   )NNNN)r4   r5   r6   r   r+   r   r   r   r9   r{   r   r|   r
   r1   r;   r<   s   @r#   r%  r%    s    y T   *.,0/3&*(
u||,(
 &(
 $D>	(

 'tn(
 d^(
 
u++	,(
 (
r%   r%  )r%  r  r   )r   F)0r7   rM   r   collections.abcr   typingr   r   r   torch.nn.functionalr   r   ri   activationsr   modeling_outputsr	   r
   modeling_utilsr   pytorch_utilsr   r   utilsr   r   configuration_pvtr   
get_loggerr4   loggerr9   r8   r{   r$   r  r'   r>   r~   r   r   r   r   r   r   r  r%  __all__r   r%   r#   <module>r:     sg  "    $ "     ! F - Q , ( 
		H	%U\\ e T V[VbVb *%")) %A) A)H	BII 	O		 Od'299 'TRYY 6+ryy +\V
 V
r   @ 0
! 0
 0
f 8
 2 8
8
v Jr%   