
    9j                        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$        eede%        eede5        eede8        eede         eede!        eede6        eede7        eede=        eede>        eedeC        eedeD        eede?        eedeA        eedeB        eede@        eedeM        eede2        eede3        eedeK        eedeL        eede        eede        eede        eede        eede        eede        eedeF        eedeF        eede9        eede"        eede        eede        eede        eede        eedeQ        eed eR        eedeS        eedeU        eedeV        eedeW        eedeX        eedeX        eedeX        eedeY        eed	eZ        eed
e        eede        eede        efdeNeO       y(      N)Callable)AnyOptionalUnionabc           	      D   t        |       }t        |      }t        ||      }g }t        |      D ]m  }|dz
  |z
  }|dz
  |z
  }|dz
  |z
  }	|dk\  r| |   nd}
|	dk\  r||	   nd}|
|k7  r|
dk7  r|dk7  rt        d|
 d| d|       |j	                  |
dk(  r|n|
       o |S )N   r   zThe size of tensor a z" must match the size of tensor b (z) at non-singleton dimension )lenmaxrangeAssertionErrorappend)r   r   dimsAdimsBndimexpandedSizesioffsetdimAdimBsizeAsizeBs               Z/media/conek/DATA/Code/OCR/venv/lib/python3.12/site-packages/torch/jit/_shape_functions.py	broadcastr      s    FEFEueD!M4[ =Aqy6!qy6! AI$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k7  rt        dt        |             t        |       dk(  s%t        |       dk(  st        dt        |              t        dt        |             D ]  }| |   dk(  st        d| d	       g }t        dt        |       dz
        D ]  }|j                  | |           |D ]  }|j                  |        |S )
N   z'Expected out to have length 2, but got       z-Expected self to have length 3 or 4, but got r
   r   zExpected self[z] to be non-zero, but got 0)r   r   r   r   )r%   r&   r   shapeelems        r   adaptive_avg_pool2dr-   <   s    
3x1}Fs3xjQRRINc$i1n;CI;G
 	
 1c$i  R7a< >!4O!PQQR E1c$i!m$ T!W TLr   c                 :    g }| D ]  }|j                  |        |S r   r   )r%   r&   r,   s      r   _copyr0   O   s'    C 

4Jr   c                     t        |       S r   r0   r%   s    r   unaryr4   V   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   Z   s    FEFEu}$UG+abgahhjk
 	
 e 	u}t#$ AI$AE>eqj 44:F5%4N 	 8Or   sizesc                    t        |      t        |       k  r$t        dt        |       dt        |        d      t        |      }t        |       }|dk(  rt        |      S g }t        |      D ]m  }|dz
  |z
  }|dz
  |z
  }|dk\  r| |   nd}||   }	|	dk(  r|dk  rt        d| d      |}	||	k7  r|dk7  rt        d	| d
|	 d      |	}|j	                  |       o |S )NzExpected len(sizes) (z) >= len(self) ()r   r
   Expected dim (z) >= 0 when targetSize is -1zExpected size (z ) == 1 when size != targetSize ()r   r   r0   r   r   )
r%   r8   r   
tensor_dimr&   r   r   dimsize
targetSizes
             r   expandrA   n   s   
5zCI#CJ</?D	{!L
 	
 u:DTJqyU|C4[ A1nv%1HtCy!1X
Qw$~cU:V%WXXJ:qy$%dV+KJ<WXY  D

4  Jr   inp0c                     t        | |      S r   )rA   )r%   r8   rB   s      r   expand_one_unusedrD      s    $r   r+   numelreturnc                    d}d }t        t        |             D ]5  }| |   dk(  r|t        d      |}| |   dk\  r	|| |   z  },t        d       ||k(  s||dkD  r||z  dk(  st        d      t        |       }|||z  ||<   |S )Nr
   r;   z"only one dimension can be inferredr   zinvalid shape dimensionszinvalid shape)r   r   r   r0   )r+   rE   newsize	infer_dimr>   r&   s         r   infer_size_implrJ      s    G#ISZ  =:$$%IJJI3Z1_uSz!G !;<<= 	!gkego6J_--
,C')IJr   c                 "    d}| D ]  }||z  }	 |S Nr
    )r8   rE   r,   s      r   rE   rE      s$    E Lr   c                 ,    t        |t        |             S r   )rJ   rE   )r%   r8   s     r   viewrO      s    5%+..r   F)implicitrP   c                    t        | |      S r   )rO   )r%   r8   rP   s      r   view_one_unusedrR      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%   rS   rT   rU   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>   rT   r&   s       r   max_dimr_      s    
tcUHd
3C8Or   xyc                     | |z  S r   rM   )r`   ra   s     r   div_rtnrc      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   )rc   )rd   re   rf   rg   rh   ri   rj   
outputSizes           r   pooling_output_shape_pad_lrrn      s     	 *q.)* 	
 'vzA/ 	
 		  Nf$	E(99#aJr   c           	      D    |dk(  rt        d      t        | ||||||      S )Nr   zstride should not be zero)r   rn   )rd   re   rf   rh   ri   rj   s         r   pooling_output_shaperp      s3     {899&:ueVXy r   inputkHkWdHdWpadHpadW	dilationH	dilationWnInputPlaneinputHeight
inputWidthoutputHeightoutputWidthc                    t        |       }|dkD  r|dkD  st        d| d| d      |dkD  r|dkD  st        d| d| d      |dkD  r|dkD  st        d| d| d      | d	   dk7  xr | d
   dk7  }|dk(  r
| d   dk7  r|s |dk(  r
|r| d   dk7  st        d| d|        |d
z  |k\  r|d
z  |k\  st        d|d
z   d| d|d
z   d| d	      |d	k\  r|d	k\  st        d| d| d      y )Nr   zExpected kW (z) > 0 and kH (z) > 0zExpected dW (z) > 0 and dH (zExpected dilationH (z) > 0 and dilationW (r
   r(   r)   r*   zInvalid input dimensions: ndim=z, input=zExpected kW//2 (z) >= padW (z) and kH//2 (z) >= padH (r:   zExpected outputWidth (z) >= 1 and outputHeight (z) >= 1r   r   )rq   rr   rs   rt   ru   rv   rw   rx   ry   rz   r{   r|   r}   r~   r   
valid_dimss                   r   pool2d_shape_checkr      sn     u:DFrAv}RDrd%HIIFrAv}RDrd%HIIMi!m"9+-B9+US
 	
 qQ058q=J	!HMAI*qQ>tfHUGTUU!GtOa4rQwi{4& 9AgYk$q2
 	
 1!2$[M 2)N&2
 	
 "3r   kernel_sizepaddingc                    t        |      dk(  st        |      dk(  st        d      |d   }t        |      dk(  r|n|d   }t        |      dk(  s't        |      dk(  st        |      dk(  st        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t        d      |d   }
t        |      dk(  r|
n|d   }t        |      dk(  st        |      dk(  st        d      |d   }t        |      dk(  r|n|d   }t        |       dk(  s%t        |       d	k(  st        d
t        |              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*   z&Expected input length 3 or 4, but got r;   )r   r   rp   r   )rq   r   rh   r   ri   rj   rr   rs   rt   ru   rv   rw   rx   ry   nbatchrz   r{   r|   r}   r~   s                       r   
max_pool2dr   /  s3    !S%5%:Y
 	
 
QB;1$+a.BK1Fq 0CK14D]
 	
 6{aVAYB
6{a	V	AYLAW!2U
 	
 1:Dw<1$4'!*DMQ#h-1"4V
 	
 I ]a/	Xa[IJ!Os5zQEc%j\RSSe*/U2YqF)K)KrJ'Rr9iXL&z2tRIVK



" 5zQ\;77\;??r   c                 *    t        | |||||      }||fS r   )r   )rq   r   rh   r   ri   rj   r&   s          r   max_pool2d_with_indicesr   z  s"     UK(I
NC:r   output_sizescale_factorsc                 $   g }|j                  | d          |j                  | d          ||t        d      |Z|t        d      t        |      dk7  rt        dt        |             |j                  |d          |j                  |d          |x|t        d      t        |      dk7  rt        dt        |             |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(   z/Expected output_size to have length 2, but got z1Expected scale_factors to have length 2, but got r)   )r   r   r   int)rq   r   r   r&   s       r   upsample_nearest2dr     s7   
 CJJuQxJJuQx!4TUU$ K  {q  A#kBRAST  	

;q>"

;q>" " K  }" CCDVCWX  	

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

3uQx-"2234Jr   mat2c                     t        |       dk7  rt        dt        |        d      t        |      dk7  rt        dt        |       d      | d   |d   k7  rt        d| d    d|d          | d   |d   gS )	Nr(   zself must be a matrix (got z dimensions)zmat2 must be a matrix (got r
   r   z.Matrix dimensions don't match for mm: self[1]=
, mat2[0]=r   r%   r   s     r   mmr     s    
4yA~:3t9+\RSS
4yA~:3t9+\RSSAw$q'<T!WIZPTUVPWyY
 	
 GT!Wr   tensorc                     t        |       dk(  rt        |      dk(  s#t        dt        |        dt        |             | d   |d   k7  rt        d| d    d|d          g }|S )Nr
   z+Expected 1D tensors for dot, got len(self)=z, len(tensor)=r   z(Dot product dimension mismatch: self[0]=z, tensor[0]=r   )r%   r   r&   s      r   dotr     s    INs6{a/9#d) Ev;-)
 	
 Aw&)6tAwi|FSTI;W
 	
 CJr   vecc                     t        |       dk(  rt        |      dk(  s#t        dt        |        dt        |             | d   |d   k7  rt        d| d    d|d          | d   gS )Nr(   r
   z0Expected 2D matrix and 1D vector, got len(self)=z, len(vec)=r   z*Matrix-vector dimension mismatch: self[1]=z	, vec[0]=r   )r%   r   s     r   mvr     s    INs3x1}>s4yk JCz#
 	
 Aw#a&8a	3q6(S
 	
 G9r   lic                 p    t        |t        |       dz         }t        |       }|j                  |d       |S rL   )rX   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 rL   )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 rL   )rX   r   r   r   )r   r>   r&   wrapped_dimr   s        r   squeezer     sh    C c"g.K3r7^ !uz

2a5!JJr!u Jr   rY   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   rX   r   )r   rY   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                 x   t        |t        |             }t        |      }t        |      dkD  rt        dt        |             |dk(  s)|t        |       k  st        d| dt        |        d      g }t	        t        |             D ]-  }||k(  r|j                  |       |j                  | |          / |S )Nr
   z"Expected len(index) <= 1, but got r   r<   z) == 0 or dim < len(self) (r:   )rX   r   multiply_integersr   r   r   )r%   r>   r   rE   result_sizer   s         r   index_selectr     s    
c$i
(Ce$E
5zA~A#e*NOO1Hc$iSE!<SYKqI
 	
  K3t9 (!8u%tAw'	(
 r   weightindicespadding_idxscale_grad_by_freqsparsec                     t        |       dk7  rt        dt        |        d      t        |      dk(  rt        | d|      S t        |      }|j	                  | d          |S )Nr(   z"Expected weight to be 2D, but got Dr
   r   )r   r   r   r0   r   )r   r   r   r   r   r?   s         r   	embeddingr     sb     6{aA#f+aPQQ
7|qFAw//>DKKq	Kr   c                       y)Nl    rM   rM   r   r   max_intr   #  s    r   startendstepc                    t        |       }|dk(  rt        d      t        ||      }||nd}||n	t               }|dk  rt        d|       |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 )Nr   z#Cannot slice a 0-dimensional tensorzExpected step > 0, but got r
   )r   r   rX   r   r0   )
r%   r>   r   r   r   r   	start_valend_val	slice_lenr&   s
             r   slicer   '  s    t9DqyBCC
d
#C*I_c')Gqy:4&ABBGI	1}T#Y	{491}		T#Y	I		DI	s))#I
+CD 1$-CHJr   tensorsc                 D    | D ]  }t        |      dk  st        d       y )Nr   z+Cannot concatenate tensor with 0 dimensionsr   )r   r   s     r   check_cat_no_zero_dimr   F  s+     Pv;! !NOOPr   tensor_sizesc                 ~    d }|D ]1  }t        |      dk(  r	|d   dk(  r|t        | t        |            }3 || }|S rl   )r   rX   )r>   r   out_dimr?   s       r   legacy_cat_wrap_dimr   L  sQ    !G 9D	Q47a<(c$i89 Nr   c                 >    t        |       dk(  xr t        |       dk(  S r   rE   r   )r   s    r   should_skipr   W  s    =A2#f+"22r   firstsecond	dimensionc                     t        |       }t        |      }||k7  rt        d| d|       t        d|      D ]0  }||k7  s	| |   ||   k7  st        d| d| |    d||    d|        y )Nz1Tensors must have same number of dimensions, got z and r   z0Sizes of tensors must match except in dimension , got z at dimension )r   r   r   )r   r   r   r   
first_dimssecond_dimsr>   s          r   check_cat_shape_except_dimr   [  s     UJf+K[ ?
|5m
 	
 Q
# )SzVC[($Fyk R :,eF3K=seM r   c                 B   t        |        t        ||       }t        |       dk  rt        d      d }| D ]  }t	        |      r|} |dgS d}t        t        |             D ])  }| |   }t	        |      rt        ||||       |||   z   }+ t        |      }|||<   |S )Nr   z(Cannot concatenate empty list of tensors)r   r   r   r   r   r   r   r0   )r   r>   not_skipped_tensorr   cat_dim_sizer   r   s          r   catr   n  s    '"
c7
+C
7|qGHH.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k(  rt        d      t        ||      }| |   }|| k  s||k\  rt        d| d| d|       |dk  r||z  }g }t        |      D ]  }||k7  s	|j	                  | |           |S )Nr   z)Cannot select from a 0-dimensional tensorzIndex z  is out of bounds for dimension z with size )r   r   rX   r   r   )r%   r>   r   r   r?   r&   r   s          r   selectr     s    t9DqyHII
d
#C9Du}UG;C5D6R
 	
 qyC4[  8JJtAw  Jr   tensor1tensor2c                 n   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 t        d      )Nr
   r(   r   r   r;   z/both arguments to matmul need to be at least 1D)
r   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     sm   g,Kg,KaK1,7G$$		kQ.'7##		kQ.r)GQ/91==		kQ.'7##		kQ. '?GBK#%{Q' 	-A  ,	-BK#%{Q' 	-A  ,	-  )F ,?"?"NOOr   c                     t        |       dkD  rt        dt        |              t        |       }|dk(  rg }|S |dk(  r| d   gS | d   | d   gS )Nr(   z1Expected tensor to have <= 2 dimensions, but got r   r
   r   )r%   self_lenr&   s      r   tr     sj    
4y1}?D	{K
 	
 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   rX   r0   r   r   )r%   r   r   ndimsr&   r   s         r   	transposer     s    IE$&D$&Dt|T{C5\  9JJtDz"$YJJtDz"JJtAw  Jr   biasc                 t    t        | t        |            }| t        ||      |k7  rt        d| d|       |S )NzBias shape z& is not broadcastable to output shape )r   r   r   r   )rq   r   r   r&   s       r   linearr     sL    
&	
"CT33& dV#I#O  Jr   mat1betaalphac                 .    t        | t        ||            S r   )r   r   )r%   r   r   r   r   s        r   addmmr     s    T2dD>**r   arrayc                 (    d}| D ]
  }|dk  s	d} |S )NFr   TrM   )r   non_negativevals      r   check_non_negativer     s*    L  7L  r   weight_sizesgroupsc           	         t        |       }t        |      }t        |      rt        d|       t        |      rt        d|       ||k7  rt        d| d| d      |d   |k  rt        d|d    d| d      |d   |z  dk7  rt        d|d    d	| d      | d
   |d
   |z  k7  rt        d| d
    d|d
   |z   d      |-t        |      d
k(  r|d   |d   k(  st        d|d    d|       t        d|      D ]]  }	| |	   d||	dz
     z  z   ||	dz
     ||	   d
z
  z  d
z   k  s*t        d| |	   d||	dz
     z  z    d||	dz
     ||	   d
z
  z  d
z    d|	        y )Nz"Padding must be non-negative, got z!Stride must be non-negative, got zExpected weight_dim (z) == k (r:   r   zExpected weight_sizes[0] (z) >= groups (z) to be divisible by groups (r
   zExpected input[1] (z) == weight_sizes[1] * groups (zFExpected bias to be None or have shape [1] with value weight_sizes[0]=r   r(   zCalculated padded input size (z)) is smaller than effective kernel size (z) at dimension )r   r   r   r   )
rq   r   r   rh   r   ri   r   k
weight_dimr   s
             r   check_shape_forwardr    s$    	E
A\"J '"A'KLL&!@IJJQ4ZL1MNNA(a(9vhaP
 	
 	Q& Q&(a(9 :ha!
 	

 Qx<?V++!%( ,Q&(),
 	
 TaDG|A4N+A/vdV=
 	

 1a[ !Hq71q5>))QUO|A23a7
 !0qAA<N1N0O PQUO|A':;a?@PQsT 	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   rh   r   ri   r   has_dilationr>   r   input_batch_size_dimweight_output_channels_dimd	dilation_kernels                  r   conv_output_sizer  8  s     Kvw& x=1$L
j/CK!"z"678{#=>?1c] 
'3HQUO	k!nq01A5]a'!a%.01F:va!e}LqP	

 r   c           	          t        |      dk7  rt        dt        |       d      t        |       dk7  rt        dt        |        d      t        | ||||||      S )Nr)   z#Expected 3D weight for conv1d, got r   z"Expected 3D input for conv1d, got r   r   r  rq   r   r   rh   r   ri   r   s          r   conv1dr  V  d     6{aB3v;-qQRR
5zQA#e*QOPPE64(FSSr   c           	          t        |      dk7  rt        dt        |       d      t        |       dk7  rt        dt        |        d      t        | ||||||      S )Nr*   z#Expected 4D weight for conv2d, got r   z"Expected 4D input for conv2d, got r  r  s          r   conv2dr  f  r  r   grad_outputbiasesc                 8    t        |      t        |      | d   gfS rL   r2   )r  rq   r   r  s       r   conv_backwardsr  v  s      <vQ(888r   output_paddingc                    |ddg}|ddg}|ddg}|ddg}t        |      dkD  }t        |       }	g }
d}d}|
j                  | |          |
j                  ||   |z         t        d|	      D ]T  }|r||dz
     nd}|||   dz
  z  }|
j                  | |   dz
  ||dz
     z  d||dz
     z  z
  |z   ||dz
     z   dz          V |
S )Nr
   r   r(   r   r   r   )rq   r   r   rh   r   r  r   ri   r  r>   r   r	  r
  r  r  r  s                   r   conv_transpose2d_inputr    s3    ~Qa&Qq6x=1$L
e*CK!"u123v89FBC1c] 	
'3HQUO	fQi!m,1X\VAE]*'!a%. ! QU#$ 		
	
 r   
transposedc	                 >   t        |      dkD  }	t        |      dkD  }
t        |       }g }d}|rdnd}|j                  | |          |r|j                  ||   |z         n|j                  ||          t        d|      D ]  }|	r||dz
     nd}|
r||dz
     nd}|rA|||   dz
  z  }|j                  | |   dz
  ||dz
     z  d||dz
     z  z
  |z   |z   dz          ^|||   dz
  z  dz   }|j                  | |   d||dz
     z  z   |z
  ||dz
     z  dz           |S )Nr   r
   r(   r  )rq   r   r   rh   r   ri   r  r  r   r  has_output_paddingr>   r   r	  r
  r  r  output_padding_r  s                      r   conv_forwardsr"    s    x=1$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$% "" 	 &)a-014FqQQ/069fQUmKaO" r   	benchmarkdeterministiccudnn_enabled
allow_tf32c                 (    t        | ||||||||	      S r   )r"  )rq   r   r   rh   r   ri   r  r  r   r#  r$  r%  r&  s                r   _conv_forwardsr(    s,     
 
r   running_meanrunning_vartrainingmomentumepsc	                 :    g }	| D ]  }
|	j                  |
        |	S r   r/   )rq   r   r   r)  r*  r+  r,  r-  r%  r&   r,   s              r   
batch_normr/    s)     C 

4Jr   c           	          t        |      dk7  rt        dt        |       d      t        |       dk7  rt        dt        |        d      t        | ||||||      S )N   z#Expected 5D weight for conv3d, got r   z"Expected 5D input for conv3d, got r  r  s          r   conv3dr2    r  r   dim_post_exprwrap_scalarc           	          |dk  r|st        d      d}| }|dz
  }| |k  s| |kD  rt        d|  d| d| d      | dk  r| |z  } | S )Nr   z7Expected wrap_scalar to be True when dim_post_expr <= 0r
   z
Dimension z( out of range (expected to be in range [z, z]))r   )r>   r3  r4  minr   s        r   rX   rX     s     I  .C
!
C
SyC#IEcU"SEQST
 	
 Qw}Jr   c                 
    g }|S r   rM   )rq   r&   s     r   zero_dim_tensorr8  !  s    CJr   c                 "    d}| D ]  }||z  }	 |S rL   rM   )r   r&   r,   s      r   r   r   &  s$    
C DjJr   inp1inp2inp3c                 h    | dk  rt        d|  d      t        t        j                  |             gS )Nr   Expected end () >= 0r   r   mathceil)r   rB   r:  r;  r<  s        r   
arange_endrC  -  s3    
Qw~cU&9::		#  r   c                     |dk  rt        d| d      || k  rt        d| d|  d      t        t        j                  || z
              gS )Nr   r>  r?  ) >= start (r:   r@  )r   r   rB   r:  r;  r<  s         r   arange_startrF  3  s[     Qw~cU&9::
U{~cU,ugQGHH		#+&'((r   c                     |dk(  rt        d      |dk  r| |k  r)t        d|  d| d      || k  rt        d| d|  d      t        t        j                  || z
  |z              gS )	Nr   zstep must not be zerozExpected start (z
) >= end (z) when step < 0r>  rE  z) when step > 0r@  )r   r   r   rB   r:  r;  r<  s          r   arange_start_steprH  =  s     qy455ax3; "5'C5H  ;  \%H  		3;$./011r   c                    t        |       t        |      k7  r$t        dt        |        dt        |       d      t        |      }g }g }t        |      D ]6  }t        ||   |      }|j	                  |       |j	                  | |          8 t        d|      D ]/  }t        |      D ]  }||   ||   k(  st        d||    d       1 |S )NExpected len(input) (z) == len(dims) (r:   r
   zRepeated dimension z in permute dimensions)r   r   r   rX   r   )rq   rY   r   	seen_dimsnewSizesr   r>   js           r   permuterN  O  s    
5zSY#CJ</?D	{!L
 	
 t9DIH4[ $T!Wd+c
#$ 1d^ q 	A|y|+$))A,7MN 	 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   rX   rN  )r%   rO  rP  self_dimnormalized_srcnormalized_dstr   ordersrc_dimsdst_dimssource_dimsdestination_dimselerest_dims                 r   movedimr\  d  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#$  )"9##C() #f+%H8_ 4%0^q!"44+ *++s   9	E	E")	E'	start_dimend_dimc                    t        |t        |             }t        |t        |             }||kD  rt        d| d| d      t        |       dk(  rdgS ||k(  rg }| D ]  }|j                  |        |S d}t	        ||dz         D ]
  }|| |   z  } g }t	        |      D ]  }|j                  | |           |j                  |       t	        |dz   t        |             D ]  }|j                  | |           |S )NzExpected start_dim (z) <= end_dim (r:   r   r
   )rX   r   r   r   r   )rq   r]  r^  r&   r,   slice_numelr   r+   s           r   flattenra    s   y#e*5IWc%j1G73I;nWIUVWXX
5zQs
G 	DJJt	
K9gk*  uQx  E9 U1X	LL7Q;E
+ U1XLr   c                     dt        |       gS Nr   r   rq   s    r   nonzero_lower_boundrf    s    s5z?r   c                 .    t        |       t        |       gS r   r   re  s    r   nonzero_upper_boundrh    s    %L#e*%%r   keepdimc                     t        |t        |             }g }t        |       D ]0  \  }}||k(  r|s|j                  d        |j                  |       2 |S rL   )rX   r   	enumerater   )r%   r>   ri  r&   r   rR  s         r   _reduce_along_dimrl    sV    
c$i
(CC  !88

1JJx ! Jr   c                 $    |g S t        | ||      S r   )rl  )r%   r>   ri  s      r   argmaxrn    s     {	T300r   c                 >   t        |       dk7  rt        dt        |        d      t        |      dk7  rt        dt        |       d      | d   |d   k7  rt        d| d    d|d          | d   |d   k7  rt        d	| d    d
|d          | d   | d   |d   gS )Nr)   z"bmm only supports 3D tensors, got r   r   z%mismatching batch dimension: self[0]=r   r(   r
   z+mismatching contracting dimension: self[2]=z
, mat2[1]=r   r   s     r   bmmrp    s    
4yA~A#d)ANOO
4yA~A#d)ANOOAw$q'3DG9JtAwiP
 	
 Aw$q'9$q'*TRSWIV
 	
 GT!Wd1g&&r   c                     t        |       gS r   rd  r3   s    r   _shape_as_tensorrr    s    I;r   r  c           	          t        |       dk(  rg }||fS || |   kD  rt        d| d| d| |          t        |       }|||<   ||fS )Nr   zk (z) is too big for dimension z	 of size )r   r   r0   )r%   r  r>   r   s       r   topkrt    sq    
4yA~ 6> tCy= aS3C5	$s)M  ts6>r   target	reductionc                 ~   t        |       }t        |      }d|cxk  rdk  sn t        d|       |dkD  rt        d|       |dk(  xr |dk(  }|s"| d   |d   k(  st        d| d    d|d          | d   }g }|'t        |      dk(  r|d   |k(  st        d	| d
|       |dk(  r|dk(  r
| d   g}	|	|fS |}	|	|fS )Nr   r(   z-Expected 0 < self_dim <= 2, but got self_dim=r
   z"Expected target_dim <= 1, but got zBatch size mismatch: self[0]=z, target[0]=r;   z:Expected weight to be None or have shape [n_classes], got z with n_classes=r   )
r%   ru  r   rv  rR  
target_dimno_batch_dim	n_classesscalar_shapereduction_shapes
             r   nll_loss_forwardr}    s!    4yHVJALXJWXXA~A*NOOq=4Z1_LT!Wq	1+DG9LL
 	
 RI L3v;!#3q	Y8N(*9+7
 	
 A~(a-7) L(( 'L((r   normalized_shapec                 @   g }t        |       t        |      z
  }|dk  r$t        dt        |        dt        |       d      t        |      D ]  }|j                  | |           t        |t        |             D ]  }|j                  d        t	        |       ||fS )Nr   rJ  z) >= len(normalized_shape) (r:   r
   )r   r   r   r   r0   )rq   r~  r|  num_unreduced_dimensionsr   s        r   native_layer_normr    s     "$O"5zC0@,AA!##CJ< 0$%&a)
 	
 +, )uQx()+SZ8 "q!"</99r   c                 6    |r| d   g}ndg}t        |       ||fS rl   r2   )rq   r   r   r)  r*  r+  _sizes          r   native_batch_normr    s*     q
<%%r   c                 .    | d   g}t        |       ||dgfS rl   r2   )rq   r   r   r)  r*  r  s         r   _batch_norm_with_updater    s$     1XJE<s**r   ignore_indexlabel_smoothingc                 (    t        | |||      d   }|S rc  )r}  )r%   ru  r   rv  r  r  result_shapes          r   cross_entropy_lossr  %  s     $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  J  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  X  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        )grA  collections.abcr   typingr   r   r   r   floatnumberr  rW   r   r"   r$   r-   r0   r4   r7   rA   rD   rJ   rE   rO   boolrR   r]   r_   rc   rn   rp   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/  r2  rX   r8  r   rC  rF  rH  rN  r\  ra  rf  rh  rl  rn  rp  rr  tuplert  r}  r  r  r  r  r  ScriptFunctionScriptFnr  dictstr__annotations__r  r  r  r  r  rM   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  	
  -
9-
-
 	-
 		-

 	-
 -
 -
 -
 -
 -
 -
 -
 -
 -
`H@9H@cH@ IH@ #Y	H@
 3iH@ H@V	9	c	 I	 #Y		
 3i	 	$9$$s)$$ DK($N
T#Y 
d3i 
d3i c T#Y T#Y $s) # d3i 	S	 	 	T#Y d3i  tCy s 49 * $I#Y  	
  
s)&.sm:B3-OR>P4S	? PS T#Y 3S	 39"3i47@C&d3i s 2(4S	? ( (c  S &&PDI &PS	 &PR"DI "DI S  "$s) T#Y htCy6I +S	 +c +$s) +3 +s +d3i D 191s)1 49
1 I	1
 #Y1 3i1 1lS	c 49
 I	
 #Y 3i <T9TIT 49
T I	T
 #YT 3iT T T9TIT 49
T I	T
 #YT 3iT T 9c999 I9 T#Y	9 !%"&#'*.$($9$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 * $s)  T#Y  T#Y  4PS9  B49  s 6tCy &tCy &	DI 	C 	$ 	 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   