
    iݰ                        U d dl Z d dlmZ d dlmZmZmZ eeef   Z	d dl
Z
dee   dee   fdZdee   dee   dee   fdZdee   dedee   fd	Zd
ee   dee   fdZd
ee   fdZd
ee   fdZdee   dee   fdZd
ee   dee   fdZd
ee   dee   defdZdee   dedee   fdZdee   fdZd
ee   dee   fdZddd
ee   dee   defdZd
ee   deee      ded efd!Zd
ee   d"edefd#Zd$ed%efd&Zd'ed(ed)ed*ed+ed,ed-efd.Zd'ed(ed)ed+ed,ed-efd/Zd0ee   d1ed2ed3ed4ed5ed6ed7ed8ed9ed:ed;ed<ed=efd>Zd0ee   d?ee   d+ee   d@ee   d,ee   d-efdAZ d0ee   d?ee   d+ee   d@ee   d,ee   d-efdBZ!d0ee   dCeee      dDeee      fdEZ"d
ee   dFee   fdGZ#d
ee   dHee   fdIZ$d
ee   dJee   fdKZ%dLee   d"efdMZ&dLee   fdNZ'dLee   d"efdOZ(dLee   dPee   fdQZ)d
ee   d"edRee   fdSZ*	 	 	 ddTee   dUee   dVedWedXef
dYZ+dZ Z,d
ee   d"ed[ee   d\ee   d]ef
d^Z-d_eee      fd`Z.d"edaeee      fdbZ/dHee   fdcZ0ddee   deee   dfedRefdgZ1d_eee      d"efdhZ2d_eee      d"efdiZ3d
ee   d"edRefdjZ4dkee   dlee   fdmZ5d
ee   fdnZ6d
ee   doedpefdqZ7d0ee   dTee   dreee      fdsZ8d
ee   dtee   dFee   duedvef
dwZ9dxee   defdyZ:d0ee   dzee   dreee      d+ee   d@ee   d,ee   d{efd|Z;d}ee   d~ee   dreee      d+ee   d@ee   d,ee   d{efdZ<d0ee   dTee   dreee      d+ee   d@ee   d,ee   d{efdZ=d0ee   dTee   dreee      d+ee   d@ee   d,ee   d{efdZ>dee   d0ee   dTee   deee      fdZ?	 	 	 	 	 	 dd0ee   dTee   dreee      d+eee      d@eee      deee      d{ed,eee      dee   fdZ@d0ee   dTee   dreee      d+ee   d@ee   d,ee   dedee   d{edee   fdZAd0ee   dTee   dreee      d+ee   d@ee   d,ee   dedee   d{edededededee   fdZBd0ee   dTeee      dreee      deee      deee      dedededefdZCd0ee   dTee   dreee      d+ee   d@ee   d,ee   d{efdZDdd"ededefdZEd0efdZFdLee   fdZGd\e	dedededef
dZHd[e	d\e	dedededefdZId[e	d\e	d]e	dedededefdZJd0ee   dPee   fdZKd
ee   dee   dee   dee   fdZLd0ee   dedefdZMd0ee   fdZNd0ee   fdZOd
ee   d"edefdZP	 dd
ee   d"ee   dedee   fdZQd
ee   dFee   dee   fdZRd
ee   dee   fdZSdd
ee   ded"edeTee   ee   f   fdZUd
ee   dee   dTeee      dedeTee   ee   f   f
dZVd0ee   dee   deTee   ee   ee   f   fdZWd0ee   dTeee      dreee      deee      deee      dedeTee   ee   ee   f   fdZXd0ee   dTeee      dreee      deee      deee      deTee   ee   ee   ee   f   fdZY	 	 	 	 dd
ee   dee   dTeee      dedededee   fdZZ	 e
j                  j                  Z]i a^e_e`e]f   ead<   i Zbe_e`eTe]e]f   f   ead<   i Zce_ee]f   ead<   defdZdde`defdZede`dedefdÄZf eede        eede        eede        eede        eedeF        eedeF        eede        eede        eedeH        eedeI        eedeJ        eede'        eede(        eede)        eede&        eede-        eede4        eede*        eede        eede        eede        eede        eede+        eede#        eede$        eede%        eede5        eede8        eede         eede!        eede6        eede7        eede=        eede>        eedeC        eedeD        eede?        eedeA        eedeB        eede@        eedeM        eede2        eede3        eedeK        eedeL        eede        eede        eede        eede        eede        eede        eedeF        eedeF        eede9        eede"        eede        eede        eede        eede        eedeQ        eed eR        eedeS        eedeU        eedeV        eedeW        eedeX        eedeX        eedeX        eedeY        eed	eZ        eed
e        eede        eede        efdeNeO       y(      N)Callable)AnyOptionalUnionabc           	      D   t        |       }t        |      }t        ||      }g }t        |      D ]m  }|dz
  |z
  }|dz
  |z
  }|dz
  |z
  }	|dk\  r| |   nd}
|	dk\  r||	   nd}|
|k7  r|
dk7  r|dk7  rt        d|
 d| d|       |j	                  |
dk(  r|n|
       o |S )N   r   zThe size of tensor a z" must match the size of tensor b (z) at non-singleton dimension )lenmaxrangeAssertionErrorappend)r   r   dimsAdimsBndimexpandedSizesioffsetdimAdimBsizeAsizeBs               T/var/www/html/engine/venv/lib/python3.12/site-packages/torch/jit/_shape_functions.py	broadcastr      s    FEFEueD!M4[ =Aqy6!qy6! AI$A AI$AE>eqjUaZ 'w.PQVPWWtuvtwx  	eqjUe<=     cc                 .    t        t        | |      |      S Nr   r   r   r   s      r   broadcast_threer"   4   s    Yq!_a((r   c                     t        | |      S r   r    r!   s      r   broadcast_one_threer$   8   s    Q?r   selfoutc                 @   t        |      dk(  sJ t        |       dk(  st        |       dk(  sJ t        dt        |             D ]  }| |   dk7  rJ  g }t        dt        |       dz
        D ]  }|j                  | |           |D ]  }|j                  |        |S )N         r
   r   )r   r   r   )r%   r&   r   shapeelems        r   adaptive_avg_pool2dr-   <   s    s8q==t9>SY!^++1c$i  Aw!|| E1c$i!m$ T!W TLr   c                 :    g }| D ]  }|j                  |        |S r   r   )r%   r&   r,   s      r   _copyr0   J   s'    C 

4Jr   c                     t        |       S r   r0   r%   s    r   unaryr4   Q   s    ;r   c                    t        |       }t        |      }||kD  rt        d| d| d      t        |      D ]B  }||z
  |z   }| |   }|dk\  r||   nd}||k7  s"|dk7  s(t        dj                  |||             t	        |       S )NzThe dims of tensor b (z6) must be less than or equal to the dims of tensor a (z) r   r
   zZThe size of tensor a {} must match the size of tensor b ({}) at non-singleton dimension {})r   r   r   formatr0   )r   r   r   r   r   r   r   r   s           r   broadcast_inplacer7   U   s    FEFEu}$UG+abgahhjk
 	
 e 	u}t#$ AI$AE>eqj 44:F5%4N 	 8Or   sizesc                 D   t        |      t        |       k\  sJ t        |      }t        |       }|dk(  rt        |      S g }t        |      D ]P  }|dz
  |z
  }|dz
  |z
  }|dk\  r| |   nd}||   }	|	dk(  r	|dk\  sJ |}	||	k7  r	|dk(  sJ |	}|j                  |       R |S )Nr   r
   )r   r0   r   r   )
r%   r8   r   
tensor_dimr&   r   r   dimsize
targetSizes
             r   expandr?   i   s    u:T"""u:DTJqyU|C4[ A1nv%1HtCy!1X
!8O8J:199D

4 Jr   inp0c                     t        | |      S r   )r?   )r%   r8   r@   s      r   expand_one_unusedrB      s    $r   r+   numelreturnc                    d}d }t        t        |             D ]5  }| |   dk(  r|t        d      |}| |   dk\  r	|| |   z  },t        d       ||k(  s||dkD  r||z  dk(  st        d      t        |       }|||z  ||<   |S )Nr
   r:   z"only one dimension can be inferredr   zinvalid shape dimensionszinvalid shape)r   r   r   r0   )r+   rC   newsize	infer_dimr<   r&   s         r   infer_size_implrH      s    G#ISZ  =:$$%IJJI3Z1_uSz!G !;<<= 	!gkego6J_--
,C')IJr   c                 "    d}| D ]  }||z  }	 |S Nr
    )r8   rC   r,   s      r   rC   rC      s$    E Lr   c                 ,    t        |t        |             S r   )rH   rC   )r%   r8   s     r   viewrM      s    5%+..r   F)implicitrN   c                    t        | |      S r   )rM   )r%   r8   rN   s      r   view_one_unusedrP      s    er   opt_dimskeep_dimdtc           	      :   g }|t        |      dk(  rt        t        t        |                   }n|}t        t        |             D ]Q  }d}|D ]  }|t        |t        |             k(  sd} |r|s,|j	                  d       >|j	                  | |          S |S )Nr   FTr
   )r   listr   maybe_wrap_dimr   )	r%   rQ   rR   rS   r&   dimsidxis_mean_dim
reduce_dims	            r   sum_mean_dimr[      s     C3x=A-uSY/0SY 	"! 	#JnZT;;"	# 

1JJtCy!	" Jr   r<   c                 (    t        | |g|d       }||fS r   )r[   )r%   r<   rR   r&   s       r   max_dimr]      s    
tcUHd
3C8Or   xyc                     | |z  S r   rK   )r^   r_   s     r   div_rtnra      s    6Mr   	inputSize
kernelSizepad_lpad_rstridedilation	ceil_modec                     t        | |z   |z   ||dz
  z  z
  dz
  |r|dz
  ndz   |      dz   }|r|dz
  |z  | |z   k\  r|dz
  }|S Nr
   r   )ra   )rb   rc   rd   re   rf   rg   rh   
outputSizes           r   pooling_output_shape_pad_lrrl      s     	 *q.)* 	
 'vzA/ 	
 		  Nf$	E(99#aJr   c           	      <    |dk7  sJ d       t        | ||||||      S )Nr   zstride should not be zeero)rl   )rb   rc   rd   rf   rg   rh   s         r   pooling_output_shapern      s2     Q;444;&:ueVXy r   inputkHkWdHdWpadHpadW	dilationH	dilationWnInputPlaneinputHeight
inputWidthoutputHeightoutputWidthc                    t        |       }|dkD  r|dkD  sJ |dkD  r|dkD  sJ |dkD  r|dkD  sJ | d   dk7  xr | d   dk7  }|dk(  r
| d   dk7  r|s|dk(  r
|r| d   dk7  sJ |dz  |k\  r|dz  |k\  sJ |dk\  r|dk\  sJ y )Nr   r
   r(   r)   r*   r   )ro   rp   rq   rr   rs   rt   ru   rv   rw   rx   ry   rz   r{   r|   r   
valid_dimss                   r   pool2d_shape_checkr      s      u:D6b1f6b1fq=Y]**qQ058q=J	!HMAI*qQ		8 7d?rQw$..! 111 1r   kernel_sizepaddingc                 p   t        |      dk(  st        |      dk(  sJ d       |d   }t        |      dk(  r|n|d   }t        |      dk(  s#t        |      dk(  st        |      dk(  sJ d       t        |      dk(  r|n|d   }t        |      dk(  r|}	nt        |      dk(  r|}	n|d   }	t        |      dk(  st        |      dk(  sJ d       |d   }
t        |      dk(  r|
n|d   }t        |      dk(  st        |      dk(  sJ d       |d   }t        |      dk(  r|n|d   }t        |       dk(  st        |       d	k(  sJ t        |       d	k(  r| d
   nd}| d   }| d   }| d   }t        |||
|||      }t        ||||	||      }t        | ||||	|
||||||||       t        |       dk(  r|||gS ||||gS )Nr
   r(   zKmax_pool2d: kernel_size must either be a single int, or a tuple of two intsr   zOmax_pool2d: stride must either be omitted, a single int, or a tuple of two intszGmax_pool2d: padding must either be a single int, or a tuple of two intszHmax_pool2d: dilation must be either a single int, or a tuple of two intsr)   r*   r:   )r   rn   r   )ro   r   rf   r   rg   rh   rp   rq   rr   rs   rt   ru   rv   rw   nbatchrx   ry   rz   r{   r|   s                       r   
max_pool2dr     s"    {q C$4$9 U9 
QB;1$+a.Bv;!s6{a/3v;!3C YC 6{aVAYB
6{a	V	AYw<1G 1 Q1 1:Dw<1$4'!*Dx=AX!!3 R3 I ]a/	Xa[Iu:?c%jAo--e*/U2YqF)K)KrJ'Rr9iXL&z2tRIVK



" 5zQ\;77\;??r   c                 *    t        | |||||      }||fS r   )r   )ro   r   rf   r   rg   rh   r&   s          r   max_pool2d_with_indicesr   Z  s"     UK(I
NC:r   output_sizescale_factorsc                    g }|j                  | d          |j                  | d          |	|J d       |A|J d       t        |      dk(  sJ |j                  |d          |j                  |d          |_|J d       t        |      dk(  sJ |j                  t        | d   |d   z               |j                  t        | d   |d   z               |S )Nr   r
   z5Either output_size or scale_factors must be presentedz9Must specify exactly one of output_size and scale_factorsr(   r)   )r   r   int)ro   r   r   r&   s       r   upsample_nearest2dr   f  s   
 CJJuQxJJuQx!4IIIq$ 	
G	
$ ;1$$$

;q>"

;q>" " 	
G	
" =!Q&&&

3uQx-"2234

3uQx-"2234Jr   mat2c                     t        |       dk(  sJ d       t        |      dk(  sJ d       | d   |d   k(  sJ | d   |d   gS )Nr(   zself must be a matrixzmat2 must be a matrixr
   r   r~   r%   r   s     r   mmr     sW    t9>222>t9>222>7d1gGT!Wr   tensorc                 `    t        |       dk(  rt        |      dk(  sJ | d   |d   k(  sJ g }|S rj   r~   )r%   r   r&   s      r   dotr     s>    t9>c&kQ...7fQiCJr   vecc                 d    t        |       dk(  rt        |      dk(  sJ | d   |d   k(  sJ | d   gS Nr(   r
   r   r~   )r%   r   s     r   mvr     s?    t9>c#h!m++7c!fG9r   lic                 p    t        |t        |       dz         }t        |       }|j                  |d       |S rJ   )rV   r   r0   insert)r   r<   r&   s      r   	unsqueezer     s2    
c"gk
*C
)CJJsAJr   c                 v    g }t        t        |             D ]  }| |   dk7  s|j                  | |          ! |S rJ   )r   r   r   )r   r&   r   s      r   squeeze_nodimr     s@    C3r7^ a5A:JJr!u Jr   c                     g }t        |t        |             }t        t        |             D ]9  }||k(  r| |   dk7  s|j                  | |          &|j                  | |          ; |S rJ   )rV   r   r   r   )r   r<   r&   wrapped_dimr   s        r   squeezer     sh    C c"g.K3r7^ !uz

2a5!JJr!u Jr   rW   c                 F   t        |      dk(  r| S t        |      }t        t        |            D ]  }t        ||   t        |             ||<    g }t        t        |             D ]8  }| |   dk(  r||vs|j	                  | |          %|j	                  | |          : |S Nr   r
   )r   r0   r   rV   r   )r   rW   wrapped_dimsr   results        r   squeeze_dimsr     s    
4yA~	;L3t9 C(a#b'BQCF3r7^ !a5A:$be$MM"Q% ! Mr   indexc                    t        |t        |             }t        |      }t        |      dk  sJ |dk(  s|t        |       k  sJ g }t        t        |             D ]-  }||k(  r|j	                  |       |j	                  | |          / |S rj   )rV   r   multiply_integersr   r   )r%   r<   r   rC   result_sizer   s         r   index_selectr     s    
c$i
(Ce$Eu:??!8sSY&&K3t9 (!8u%tAw'	(
 r   weightindicespadding_idxscale_grad_by_freqsparsec                     t        |       dk(  sJ t        |      dk(  rt        | d|      S t        |      }|j                  | d          |S r   )r   r   r0   r   )r   r   r   r   r   r=   s         r   	embeddingr     sO     v;!
7|qFAw//>DKKq	Kr   c                       y)Nl    rK   rK   r   r   max_intr     s    r   startendstepc                 Z   t        |       }|dk7  sJ t        ||      }||nd}||n	t               }|dkD  sJ |t               k(  rd}|dk  r|| |   z  }|dk  r|| |   z  }|dk  rd}n|| |   kD  r| |   }||k  r|}n|| |   k\  r| |   }||z
  }t        |       }	||z   dz
  |z  |	|<   |	S r   )r   rV   r   r0   )
r%   r<   r   r   r   r   	start_valend_val	slice_lenr&   s
             r   slicer     s     t9D199
d
#C*I_c')G!8O8GI	1}T#Y	{491}		T#Y	I		DI	s))#I
+CD 1$-CHJr   tensorsc                 2    | D ]  }t        |      dkD  rJ  y Nr   r~   )r   r   s     r   check_cat_no_zero_dimr     s!     6{Qr   tensor_sizesc                 ~    d }|D ]1  }t        |      dk(  r	|d   dk(  r|t        | t        |            }3 || }|S rj   )r   rV   )r<   r   out_dimr=   s       r   legacy_cat_wrap_dimr   	  sQ    !G 9D	Q47a<(c$i89 Nr   c                 >    t        |       dk(  xr t        |       dk(  S r   rC   r   )r   s    r   should_skipr     s    =A2#f+"22r   firstsecond	dimensionc                     t        |       }t        |      }||k(  sJ d       t        d|      D ]  }||k7  s	| |   ||   k(  rJ d        y )Nz+Tensors must have same number of dimensionsr   z/Sizes of tensors must match except in dimension)r   r   )r   r   r   r   
first_dimssecond_dimsr<   s          r   check_cat_shape_except_dimr     sh     UJf+K$S&SS$Q
# ):, A,r   c                 0   t        |        t        ||       }t        |       dkD  sJ d }| D ]  }t        |      r|} |dgS d}t	        t        |             D ])  }| |   }t        |      rt        ||||       |||   z   }+ t        |      }|||<   |S r   )r   r   r   r   r   r   r0   )r   r<   not_skipped_tensorr   cat_dim_sizer   r   s          r   catr   %  s    '"
c7
+Cw<!.2 (6"!'( !s
L3w<  66"&'963J'&+5L	6 *+K#Kr   c                 f    g }| D ]  }t        ||      }|j                  |       ! t        ||      S r   )r   r   r   )r   r<   unsqueezed_tensorsr   
unsqueezeds        r   stackr   =  sA    *, .vs+
!!*-. !3''r   c                     t        |       }|dk7  sJ t        ||      }| |   }|| k  s||k\  rJ |dk  r||z  }g }t        |      D ]  }||k7  s	|j                  | |           |S r   )r   rV   r   r   )r%   r<   r   r   r=   r&   r   s          r   selectr   E  s    t9D199
d
#C9D$//qyC4[  8JJtAw  Jr   tensor1tensor2c                 f   t        |       }t        |      }|dk(  r|dk(  rt        | |      S |dk(  r|dk(  rt        | |      S |dk(  r%|dk(  r t        t	        t        | d      |      d      S |dk(  r|dk(  rt	        | |      S |dk\  r|dk\  r|dkD  r| d   nd}g }t        |dz
        D ]  }|j                  | |           |d   }g }t        |dz
        D ]  }|j                  ||           t        ||      }	|	}
|dkD  r|
j                  |       |dkD  r|
j                  |       |
S J d       )Nr
   r(   r   r   r:   z0both  arguments to matmul need to be at least 1D)	r   r   r   r   r   r   r   r   r   )r   r   dim_tensor1dim_tensor2nbatch_tensor1r   pbatch_tensor2expand_batch_portionoutput_shapes              r   matmulr   T  sl   g,Kg,KaK1,7G$$		kQ.'7##		kQ.r)GQ/91==		kQ.'7##		kQ. '?GBK#%{Q' 	-A  ,	-BK#%{Q' 	-A  ,	-  )F ,?"?"HHHur   c                 t    t        |       dk  sJ t        |       }|dk(  rg }|S |dk(  r| d   gS | d   | d   gS )Nr(   r   r
   r~   )r%   self_lenr&   s      r   tr   }  sR    t9>>4yH1}
	QQyQa!!r   dim0dim1c                     t        |       }t        ||      }t        ||      }||k(  rt        |       S g }t        |      D ]J  }||k(  r|j	                  | |          ||k(  r|j	                  | |          7|j	                  | |          L |S r   )r   rV   r0   r   r   )r%   r   r   ndimsr&   r   s         r   	transposer     s    IE$&D$&Dt|T{C5\  9JJtDz"$YJJtDz"JJtAw  Jr   biasc                 V    t        | t        |            }|t        ||      |k(  sJ |S r   )r   r   r   )ro   r   r   r&   s       r   linearr     s2    
&	
"Cs#s***Jr   mat1betaalphac                 .    t        | t        ||            S r   )r   r   )r%   r   r   r   r   s        r   addmmr     s    T2dD>**r   arrayc                 (    d}| D ]
  }|dk  s	d} |S )NFr   TrK   )r   non_negativevals      r   check_non_negativer     s*    L  7L  r   weight_sizesgroupsc                 l   t        |       }t        |      }t        |      rJ t        |      rJ ||k(  sJ |d   |k\  sJ |d   |z  dk(  sJ | d   |d   |z  k(  sJ |t        |      dk(  r|d   |d   k(  sJ t        d|      D ]*  }	| |	   d||	dz
     z  z   ||	dz
     ||	   dz
  z  dz   k\  r*J  y Nr   r
   r(   )r   r   r   )
ro   r   r   rf   r   rg   r   k
weight_dimr   s
             r   check_shape_forwardr    s    	E
A\"J "'***!&)))???f$$$Of$***8|A////<CINtAw,q//IJJ1a[ 
a1wq1u~--QUO|A23a7
 	
 

r   
input_sizeweight_sizec           	      j   t        | ||||||       t        |      dkD  }t        |       }g }	d}
d}|	j                  | |
          |	j                  ||          t        d|      D ]K  }|r||dz
     nd}|||   dz
  z  dz   }|	j                  | |   d||dz
     z  z   |z
  ||dz
     z  dz          M |	S )Nr   r(   r
   )r  r   r   r   )r  r  r   rf   r   rg   r   has_dilationr<   r   input_batch_size_dimweight_output_channels_dimd	dilation_kernels                  r   conv_output_sizer    s     Kvw& x=1$L
j/CK!"z"678{#=>?1c] 
'3HQUO	k!nq01A5]a'!a%.01F:va!e}LqP	

 r   c           	      d    t        |      dk(  sJ t        |       dk(  sJ t        | ||||||      S )Nr)   r   r  ro   r   r   rf   r   rg   r   s          r   conv1dr    =     v;!u:??E64(FSSr   c           	      d    t        |      dk(  sJ t        |       dk(  sJ t        | ||||||      S )Nr*   r  r  s          r   conv2dr    r  r   grad_outputbiasesc                 8    t        |      t        |      | d   gfS rJ   r2   )r  ro   r   r  s       r   conv_backwardsr    s      <vQ(888r   output_paddingc                    |ddg}|ddg}|ddg}|ddg}t        |      dkD  }t        |       }	g }
d}d}|
j                  | |          |
j                  ||   |z         t        d|	      D ]T  }|r||dz
     nd}|||   dz
  z  }|
j                  | |   dz
  ||dz
     z  d||dz
     z  z
  |z   ||dz
     z   dz          V |
S )Nr
   r   r(   r   r   r   )ro   r   r   rf   r   r  r   rg   r  r<   r   r  r  r	  r
  r  s                   r   conv_transpose2d_inputr    s3    ~Qa&Qq6x=1$L
e*CK!"u123v89FBC1c] 	
'3HQUO	fQi!m,1X\VAE]*'!a%. ! QU#$ 		
	
 r   
transposedc	                 >   t        |      dkD  }	t        |      dkD  }
t        |       }g }d}|rdnd}|j                  | |          |r|j                  ||   |z         n|j                  ||          t        d|      D ]  }|	r||dz
     nd}|
r||dz
     nd}|rA|||   dz
  z  }|j                  | |   dz
  ||dz
     z  d||dz
     z  z
  |z   |z   dz          ^|||   dz
  z  dz   }|j                  | |   d||dz
     z  z   |z
  ||dz
     z  dz           |S r   r  )ro   r   r   rf   r   rg   r  r  r   r  has_output_paddingr<   r   r  r  r	  r
  output_padding_r  s                      r   conv_forwardsr   8  s    x=1$L^,q0
e*CK&0au1236"<=FG6"<=>1c] '3HQUO	3E.Q/1&)a-0FqAA.ga!en$% "" 	 &)a-014FqQQ/069fQUmKaO" r   	benchmarkdeterministiccudnn_enabled
allow_tf32c                 (    t        | ||||||||	      S r   )r   )ro   r   r   rf   r   rg   r  r  r   r!  r"  r#  r$  s                r   _conv_forwardsr&  c  s,     
 
r   running_meanrunning_vartrainingmomentumepsc	                 :    g }	| D ]  }
|	j                  |
        |	S r   r/   )ro   r   r   r'  r(  r)  r*  r+  r#  r&   r,   s              r   
batch_normr-    s)     C 

4Jr   c           	      d    t        |      dk(  sJ t        |       dk(  sJ t        | ||||||      S )N   r  r  s          r   conv3dr0    r  r   dim_post_exprwrap_scalarc                 X    |dk  r|sJ d}| }|dz
  }| |k  s| |kD  rJ | dk  r| |z  } | S r   rK   )r<   r1  r2  minr   s        r   rV   rV     sQ    {.C
!
Cc	S3Y''
Qw}Jr   c                 
    g }|S r   rK   )ro   r&   s     r   zero_dim_tensorr6    s    CJr   c                 "    d}| D ]  }||z  }	 |S rJ   rK   )r   r&   r,   s      r   r   r     s$    
C DjJr   inp1inp2inp3c                 N    | dk\  sJ t        t        j                  |             gS r   r   mathceil)r   r@   r8  r9  r:  s        r   
arange_endr?    s#    !8O8		#  r   c                 b    |dk\  sJ || k\  sJ t        t        j                  || z
              gS r   r<  )r   r   r@   r8  r9  r:  s         r   arange_startrA    s6     !8O8%<<		#+&'((r   c                     |dk7  sJ |dk  r| |k\  s	J || k\  sJ t        t        j                  || z
  |z              gS r   r<  )r   r   r   r@   r8  r9  r:  s          r   arange_start_steprC    sO     199ax||e||		3;$./011r   c                 :   t        |       t        |      k(  sJ t        |      }g }g }t        |      D ]6  }t        ||   |      }|j                  |       |j                  | |          8 t        d|      D ]  }t        |      D ]  }||   ||   k7  rJ  ! |S rJ   )r   r   rV   r   )ro   rW   r   	seen_dimsnewSizesr   r<   js           r   permuterH    s    u:T"""t9DIH4[ $T!Wd+c
#$ 1d^ 0q 	0AQ<9Q<///	00 Or   sourcedestinationc                    t        |       }|dk  r| S g }g }t        t        |            D ]>  }|j                  t        ||   |             |j                  t        ||   |             @ t        |      D cg c]  }d }}t        |      D cg c]  }| }}t        |      D cg c]  }| }	}t        t        |            D ]  }||   |||   <   d|||   <   d|	||   <    g }
g }|D ]  }|dk7  s	|
j                  |        |	D ]  }|dk7  s	|j                  |        |t        |      z
  }t        |      D ]  }|
|   |||   <    t	        | |      S c c}w c c}w c c}w )Nr
   r:   )r   r   r   rV   rH  )r%   rI  rJ  self_dimnormalized_srcnormalized_dstr   ordersrc_dimsdst_dimssource_dimsdestination_dimselerest_dims                 r   movedimrV    s   4yH1} "N "N3v; HnVAYABn[^XFGH x)AR)E) ?+a+H+ ?+a+H+3v; )#1!#4nQ &("#&("#)
  K"$ $"9s#$  )"9##C() #f+%H8_ 4%0^q!"44+ *++s   9	E	E")	E'	start_dimend_dimc                    t        |t        |             }t        |t        |             }||k  sJ t        |       dk(  rdgS ||k(  rg }| D ]  }|j                  |        |S d}t        ||dz         D ]
  }|| |   z  } g }t        |      D ]  }|j                  | |           |j                  |       t        |dz   t        |             D ]  }|j                  | |           |S r   )rV   r   r   r   )ro   rW  rX  r&   r,   slice_numelr   r+   s           r   flattenr[    s   y#e*5IWc%j1G
5zQs
G 	DJJt	
K9gk*  uQx  E9 U1X	LL7Q;E
+ U1XLr   c                     dt        |       gS r   r~   ro   s    r   nonzero_lower_boundr^    s    s5z?r   c                 .    t        |       t        |       gS r   r   r]  s    r   nonzero_upper_boundr`    s    %L#e*%%r   keepdimc                     t        |t        |             }g }t        |       D ]0  \  }}||k(  r|s|j                  d        |j                  |       2 |S rJ   )rV   r   	enumerater   )r%   r<   ra  r&   r   rL  s         r   _reduce_along_dimrd     sV    
c$i
(CC  !88

1JJx ! Jr   c                 $    |g S t        | ||      S r   )rd  )r%   r<   ra  s      r   argmaxrf  ,  s     {	T300r   c                     t        |       dk(  sJ d       t        |      dk(  sJ d       | d   |d   k(  sJ d       | d   |d   k(  sJ d       | d   | d   |d   gS )Nr)   zbmm only supports 3D tensorsr   zmismatching batch dimensionr(   r
   z!mismatching contracting dimensionr~   r   s     r   bmmrh  4  s    t9>999>t9>999>7d1g<<<7d1gBBBGT!Wd1g&&r   c                     t        |       gS r   r~   r3   s    r   _shape_as_tensorrj  <  s    I;r   r   c                     t        |       dk(  rg }||fS || |   k  sJ d| d| d| |           t        |       }|||<   ||fS )Nr   zk (z) is too big for dimension z	 of size )r   r0   )r%   r   r<   r   s       r   topkrl  @  sp    
4yA~ 6> DI~ 	
!/uId3i[I	
~ ts6>r   target	reductionc                    t        |       }t        |      }d|cxk  rdk  sJ  J |dk  sJ |dk(  xr |dk(  }|s| d   |d   k(  sJ | d   }g }|t        |      dk(  r|d   |k(  sJ |dk(  r|dk(  r
| d   g}	|	|fS |}	|	|fS )Nr   r(   r
   r:   r~   )
r%   rm  r   rn  rL  
target_dimno_batch_dim	n_classesscalar_shapereduction_shapes
             r   nll_loss_forwardru  L  s     4yHVJx1??q=4Z1_LDGvay011RI L>c&kQ.6!9	3IJJA~(a-7) L(( 'L((r   normalized_shapec                     g }t        |       t        |      z
  }|dk\  sJ t        |      D ]  }|j                  | |           t        |t        |             D ]  }|j                  d        t        |       ||fS r   )r   r   r   r0   )ro   rv  rt  num_unreduced_dimensionsr   s        r   native_layer_normry  `  s     "$O"5zC0@,AA#q(((+, )uQx()+SZ8 "q!"</99r   c                 6    |r| d   g}ndg}t        |       ||fS rj   r2   )ro   r   r   r'  r(  r)  _sizes          r   native_batch_normr|  m  s*     q
<%%r   c                 .    | d   g}t        |       ||dgfS rj   r2   )ro   r   r   r'  r(  r{  s         r   _batch_norm_with_updater~  |  s$     1XJE<s**r   ignore_indexlabel_smoothingc                 (    t        | |||      d   }|S r   )ru  )r%   rm  r   rn  r  r  result_shapes          r   cross_entropy_lossr    s     $D&&)DQGLr   shape_compute_graph_mappingbounded_compute_graph_mappingscript_func_mapfuncc                    | t         vrt        j                  j                  |       }t        j                  j                  |j                         t        d      D ]T  }t        j                  j                  |j                         t        j                  j                  |j                         V |t         | <   t         |    S )Nr(   )
r  torchjitscript_C_jit_pass_inlinegraphr   _jit_pass_peephole_jit_pass_constant_propagation)r  scripted_func_s      r   process_funcr    s    ?"		((.!!-"5"56q 	IAHH''(;(;<HH33M4G4GH	I !.4  r   operator_schemac                 (    t        |      t        | <   y r   )r  r  )r  r  s     r   add_shape_compute_mappingr    s     4@3E0r   lower_bound_funcupper_bound_funcc                 B    t        |      t        |      f}|t        | <   y r   )r  r  )r  r  r  fnss       r   add_bounded_compute_mappingr    s%     ()<8H+I
JC58!/2r   z^aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)zFaten::rsub.Tensor(Tensor self, Scalar other, Scalar alpha=1) -> Tensorz:aten::dropout(Tensor input, float p, bool train) -> TensorzDaten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensorz,prim::NumToTensor.Scalar(Scalar a) -> Tensorz(prim::NumToTensor.bool(bool a) -> Tensorzuaten::zeros(int[] size, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)z{aten::to.dtype(Tensor(a) self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))zvaten::arange(Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)zaten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensorzaten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensorz*aten::squeeze(Tensor(a) self) -> Tensor(a)z7aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)z:aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)z5aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)zfaten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> Tensor(a)zAaten::select.int(Tensor(a) self, int dim, int index) -> Tensor(a)z@aten::index_select(Tensor self, int dim, Tensor index) -> Tensorzaten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> TensorzIaten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensorzhaten::_no_grad_embedding_renorm_(Tensor weight, Tensor input, float max_norm, float norm_type) -> Tensorzgaten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)z~aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensorz,aten::mm(Tensor self, Tensor mat2) -> Tensorz/aten::dot(Tensor self, Tensor tensor) -> Tensorz+aten::mv(Tensor self, Tensor vec) -> Tensorz1aten::matmul(Tensor self, Tensor other) -> TensorzFaten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensorzaten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensorzaten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)z$aten::t(Tensor(a) self) -> Tensor(a)zDaten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)zaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensorzaten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensorzaten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensorzaten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensorzaten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, int[]? bias_sizes, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)zaten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensorzaten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensorzaten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> TensorzVaten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)z0aten::cat(Tensor[] tensors, int dim=0) -> Tensorz2aten::stack(Tensor[] tensors, int dim=0) -> Tensorz6aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)zSaten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)z3aten::view(Tensor(a) self, int[] size) -> Tensor(a)z:aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)zMaten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)zaaten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensorzhaten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> TensorzZaten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)z<aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensorz;aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensorz^aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensorzbaten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)z_aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensorzraten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensorz'aten::dequantize(Tensor self) -> TensorzNquantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qczFaten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensorz-aten::bmm(Tensor self, Tensor mat2) -> Tensorz-aten::_shape_as_tensor(Tensor self) -> Tensorzraten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)zaten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)zaten::native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)zaten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)zaten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)zaten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)z_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)zaten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> TensorzCaten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> TensorzMaten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> TensorzQaten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)z&aten::nonzero(Tensor self) -> (Tensor))r:   FF)NNNNr
   N)T)NF)r:   )Nr
   ig        )gr=  collections.abcr   typingr   r   r   r   floatnumberr  rU   r   r"   r$   r-   r0   r4   r7   r?   rB   rH   rC   rM   boolrP   r[   r]   ra   rl   rn   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r  r  r  r  r  r  r   r&  r-  r0  rV   r6  r   r?  rA  rC  rH  rV  r[  r^  r`  rd  rf  rh  rj  tuplerl  ru  ry  r|  r~  r  r  ScriptFunctionScriptFnr  dictstr__annotations__r  r  r  r  r  rK   r   r   <module>r     s    $ ' ' 
sEz	$ c tCy 0)tCy )T#Y )49 )49  c d3i d3i S	 S	 c tCy (c 49 ,DI d3i s 49 S T#Y .c /tCy /c / LQ $s) DI D 
s)'S	2>BHK,$s) #  s s   	
   6  	
  2922 	2 		2
 	2 2 2 2 2 2 2 2 2 2DC@9C@cC@ IC@ #Y	C@
 3iC@ C@L	9	c	 I	 #Y		
 3i	 	9$s)$ DK(>T#Y d3i d3i c T#Y T#Y $s) # d3i 	S	 	 	T#Y d3i  tCy s 49 " $I#Y  	
 
s)&.sm:B3-OR:4S	? 
S T#Y 3S	 3
9
"3i
47
@C
d3i s 0(4S	? ( (c  S &IDI &IS	 &IR	"DI 	"DI S  "$s) T#Y htCy6I +S	 +c +$s) +3 +s +d3i D 
9
s)
 49

 I	

 #Y
 3i
 
>S	c 49
 I	
 #Y 3i <T9TIT 49
T I	T
 #YT 3iT TT9TIT 49
T I	T
 #YT 3iT T9c999 I9 T#Y	9 !%"&#'*.$($9$I$ 49
$ T#Y	$
 d3i $ T#Y'$ $ tCy!$ 
#Y$N(9(I( 49
( I	(
 #Y( 3i( ( I( ( 
#Y(V9I 49
 I	
 #Y 3i  I      
#Y89T#Y 49
 49%	
 $s)$   
 "T9TIT 49
T I	T
 #YT 3iT T	 	C 	d 	3 
$s) !F !# !S ! !3 !
)))&))14)<?)GJ)222&,2472?B2JM2UX249 DI  $s)  T#Y  T#Y  4PS9  B49  s 4tCy &tCy &	DI 	C 	$ 	 AF1
s)1"3-19=1	#Y1'd3i 'tCy 'T#Y '49 c 	tCy 	S 	s 	E$s)T#Y:N4O 	)
s))!#Y)08c0C)PS)
49d3i )(
:9
:(,S	
:
49d3ic*+
:&9&T#Y& 49
& 49%	&
 $s)$& & 49d3ic*+&+9+T#Y+ 49
+ 49%	+
 $s)$+ 49d3icDI56+ #' 	
s)	I	 T#Y	 		
 	 	 
#Y	& 88""35 T#x-0 5FH tCx/A)B$BC H,.h() .!x !Fs F( F99,49HP9 d	 Le @% J 2O Do V {	  B	 |  Y  k F V =w @, ;Y l	 G F 9	
 OQV n	 m	  E H" M KS Q G L Mv V Lf  O  f @! D JI  Q
  Q
  v  Q
  v  m  w  z \ Lc R NPU V <g Y OQU V @& S g n ` BO A? d	 h e	 x	 CU K T Lf I3 O 35E x  R  I  {  @  I  z
  c I S W ,.ACVr   