
    <ЦiVD                        S r SSKJrJrJrJrJr  SSKrSSKJr  SSK	J
r
JrJrJrJrJrJrJrJrJrJrJrJr  SS/r " S	 S\5      rS
S\ S\
 S\ S\ S\ S3-   \l         S\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\4S jrS\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\4S jr\" \S9     S!S\\   S\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\4S  jj5       rg)"z1Implementation for the Resilient backpropagation.    )castListOptionalTupleUnionN)Tensor   )_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_foreach_doc!_get_capturable_supported_devices_get_scalar_dtype_maximize_doc_params_doc_use_grad_for_differentiable_view_as_real	OptimizerParamsTRproprpropc                      ^  \ rS rSr   SSSSSS.S\S\\\4   S\\\4   S\\\4   S	\	S
\
\	   S\	S\	4U 4S jjjjrU 4S jrS r\SS j5       rSrU =r$ )r      FN)
capturableforeachmaximizedifferentiableparamslretas
step_sizesr   r   r   r   c          
      8  > [        U[        5      (       a  UR                  5       S:w  a  [        S5      eSU::  d  [        SU 35      eSUS   s=:  a  Ss=:  a	  US   :  d  O  [        SUS    SUS    35      e[	        UUUUUUUS	9n	[
        T
U ]  X5        g )
Nr	   zTensor lr must be 1-elementg        zInvalid learning rate: r         ?zInvalid eta values: z, )r    r!   r"   r   r   r   r   )
isinstancer   numel
ValueErrordictsuper__init__)selfr   r    r!   r"   r   r   r   r   defaults	__class__s             P/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/optim/rprop.pyr*   Rprop.__init__   s     b&!!bhhjAo:;;by6rd;<<T!W,s,T!W,3DG9BtAwiHII!)!
 	*    c                 P  > [         TU ]  U5        U R                   GH  nUR                  SS 5        UR                  SS5        UR                  SS5        UR                  SS5        US    H  nU R                  R                  U/ 5      n[        U5      S:w  d  M0  [        R                  " US   5      (       a  MP  [        US   5      nUS   (       a(  [        R                  " U[        5       UR                  S	9O[        R                  " U[        5       S
9US'   M     GM     g )Nr   r   Fr   r   r   r   stepdtypedevicer4   )r)   __setstate__param_groups
setdefaultstategetlentorch	is_tensorfloattensorr   r5   )r+   r:   grouppp_statestep_valr-   s         r.   r7   Rprop.__setstate__<   s    U#&&EY-Z/-u5\518_**..B/w<1$U__WV_-M-M$WV_5H
 !. $,=,? #\\(:K:MN FO	 % 'r0   c           	      r   SnUS    GH  nUR                   c  M  U[        R                  " U5      -  nUR                  U5        UR                   n	U	R                  (       a  [        S5      eUR                  U	5        U R                  U   n
[        U
5      S:X  a  US   (       a(  [        R                  " S[        5       UR                  S9O[        R                  " S[        5       S9U
S	'   [        R                  " U[        R                  S
9U
S'   UR                  R                  (       a+  [        R                  " U	[        US   US   5      5      U
S'   O[        R                  " XS   5      U
S'   UR                  U
S   5        UR                  U
S   5        UR                  U
S	   5        GM     U$ )NFr   z'Rprop does not support sparse gradientsr   r    r3   r6   r2   memory_formatprevr    	step_size)gradr=   
is_complexappend	is_sparseRuntimeErrorr:   r<   zerosr   r5   
zeros_likepreserve_formatr4   	full_likecomplex)r+   rA   r   gradsprevsr"   state_stepshas_complexrB   rL   r:   s              r.   _init_groupRprop._init_groupO   so   xAvv~5++A..KMM!66D~~"#LMMLLJJqME 5zQ \* KK*;*=ahhOR/@/BC f !& 0 0%BWBW Xf77%% */geDk5;?*E+& */T{)KE+&LLv'eK01uV}-A !D r0   c                 d   U R                  5         SnUb%  [        R                  " 5          U" 5       nSSS5        U R                   HT  n/ n/ n/ n/ n/ nUS   u  pUS   u  pUS   nUS   nU R	                  X4XVXx5      n[        UUUUUUUU	U
UUUS   US   US9  MV     U$ ! , (       d  f       Nt= f)	zPerform a single optimization step.

Args:
    closure (Callable, optional): A closure that reevaluates the model
        and returns the loss.
Nr!   r"   r   r   r   r   )	step_size_minstep_size_maxetaminusetaplusr   r   r   r   rY   ) _cuda_graph_capture_health_checkr=   enable_gradr8   rZ   r   )r+   closurelossrA   r   rV   rW   r"   rX   r_   r`   r]   r^   r   r   rY   s                   r.   r2   
Rprop.stepu   s     	--/""$y % &&E#%F"$E"$E')J(*K %fH+0+>(MI&GZ(H**uZK ++!!$%56 .'! 'B I %$s   B!!
B/rG   )g{Gz?)g      ?g333333?)gư>2   N)__name__
__module____qualname____firstlineno__r   r   r?   r   r   boolr   r*   r7   rZ   r   r2   __static_attributes____classcell__)r-   s   @r.   r   r      s     $($.*4+ !"&$++ %- + E5L!	+
 %,'+ + $+ + + +<&$L "/ "/r0   a
  Implements the resilient backpropagation algorithm.

    .. math::
       \begin{aligned}
            &\rule{110mm}{0.4pt}                                                                 \\
            &\textbf{input}      : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta)
                \text{ (objective)},                                                             \\
            &\hspace{13mm}      \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min}
                \text{ (step sizes)}                                                             \\
            &\textbf{initialize} :   g^0_{prev} \leftarrow 0,
                \: \eta_0 \leftarrow \text{lr (learning rate)}                                   \\
            &\rule{110mm}{0.4pt}                                                                 \\
            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
            &\hspace{5mm} \textbf{for} \text{  } i = 0, 1, \ldots, d-1 \: \mathbf{do}            \\
            &\hspace{10mm}  \textbf{if} \:   g^i_{prev} g^i_t  > 0                               \\
            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+},
                \Gamma_{max})                                                                    \\
            &\hspace{10mm}  \textbf{else if}  \:  g^i_{prev} g^i_t < 0                           \\
            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
                \Gamma_{min})                                                                    \\
            &\hspace{15mm}  g^i_t \leftarrow 0                                                   \\
            &\hspace{10mm}  \textbf{else}  \:                                                    \\
            &\hspace{15mm}  \eta^i_t \leftarrow \eta^i_{t-1}                                     \\
            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t)             \\
            &\hspace{5mm}g_{prev} \leftarrow  g_t                                                \\
            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
       \end{aligned}

    For further details regarding the algorithm we refer to the paper
    `A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_.
    z
    Args:
        a{  
        lr (float, optional): learning rate (default: 1e-2)
        etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that
            are multiplicative increase and decrease factors
            (default: (0.5, 1.2))
        step_sizes (Tuple[float, float], optional): a pair of minimal and
            maximal allowed step sizes (default: (1e-6, 50))
        z	
        z

    r   rV   rW   r"   rX   r]   r^   r_   r`   r   r   r   rY   c                X   [        U 5       GH  u  pX   nU	(       d  UOU* nX-   nX=   nXM   n[        R                  R                  5       (       dd  U
(       a]  [	        5       nUR
                  R                  UR
                  R                  :X  a  UR
                  R                  U;   d   SU S35       eUS-  n[        R                  " U5      (       aX  [        R                  " U5      n[        R                  " U5      n[        R                  " U5      n[        R                  " U5      nU(       a.  UR                  UR                  5       5      R                  5       nOUR                  U5      R                  5       nU
(       a  UR                  [        R                  " UR                  S5      UU5      5        UR                  [        R                  " UR                  S5      UU5      5        UR                  [        R                  " UR!                  S5      SU5      5        O<UUUR                  S5      '   UUUR                  S5      '   SUUR!                  S5      '   UR#                  U5      R%                  XV5        UR                  [        R&                  S9nU
(       a7  UR                  [        R                  " UR!                  U5      SU5      5        OSUUR!                  U5      '   UR)                  UR                  5       USS9  UR                  U5        GM     g )NIIf capturable=True, params and state_steps must be on supported devices: .r	   r   rH   value)	enumerater=   compileris_compilingr   r5   typerM   view_as_realmulclonesigncopy_wheregtlteqmul_clamp_rS   addcmul_)r   rV   rW   r"   rX   r]   r^   r_   r`   r   r   r   rY   iparamrL   rJ   rK   r2   capturable_supported_devicesr|   s                        r.   _single_tensor_rpropr      sm     f%x#t$xM	~ ~~**,,+L+N(!!T[[%5%55LL%%)EE{ [[wZxxyz{F 		E""%%d+D%%d+D&&u-E**95I88DJJL)..0D88D>&&(DJJu{{4771:w=>JJu{{4771:x>?JJu{{4771:q$78&D'D D 	t##MA zz(=(=z>JJu{{4778#4a>?&'D"# 	tyy{IR8

4c &r0   c          
        ^ [        U 5      S:X  a  g U(       a   S5       e[        R                  R                  5       (       d?  U
(       a8  [	        5       m[        U4S j[        X5       5       5      (       d   ST S35       e[        R                  " XX#U/5      nUR                  5        GHS  u  u  nnnnnn[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        R                  R                  5       (       d>  US   R                  (       a*  [        R                  " U[        R                  " SSS9SS	9  O[        R                  " US
5        U(       a  [!        UUUU5        [        R"                  " UU5      nU	(       a  [        R$                  " U5        [        R&                  " UU5        U	(       a  [        R$                  " U5        Un[        R(                  " U5        U
(       a  U H  nUR+                  [        R,                  " UR/                  S5      UU5      5        UR+                  [        R,                  " UR1                  S5      UU5      5        UR+                  [        R,                  " UR3                  S5      S
U5      5        M     OEU H?  nUUUR/                  S5      '   UUUR1                  S5      '   S
UUR3                  S5      '   MA     [        R4                  " UU5        U H  nUR7                  XV5        M     [9        U5      n[;        [        U5      5       HB  nUU   R+                  [        R,                  " UU   R3                  U5      SUU   5      5        MD     AU Vs/ s H  nUR=                  5       PM     nn[        R>                  " UUUSS9  GMV     g s  snf )Nr   z#_foreach ops don't support autogradc              3      >#    U  HT  u  pUR                   R                  UR                   R                  :H  =(       a    UR                   R                  T;   v   MV     g 7frg   )r5   rx   ).0rB   r2   r   s      r.   	<genexpr>&_multi_tensor_rprop.<locals>.<genexpr>:  sN      
 4 HHMMT[[--- >!==>3s   AArp   rq   r$   cpu)r5   )alphar	   rr   rs   ) r<   r=   rv   rw   r   allzipr   "_group_tensors_by_device_and_dtypevaluesr   r   r   is_cpu_foreach_add_r@   r   _foreach_mul_foreach_neg__foreach_copy__foreach_sign_r}   r~   r   r   r   _foreach_mul_r   listranger|   _foreach_addcmul_) r   rV   rW   r"   rX   r]   r^   r_   r`   r   r   r   rY   grouped_tensorsgrouped_params_grouped_grads_grouped_prevs_grouped_step_sizes_grouped_state_steps__grouped_paramsgrouped_gradsgrouped_prevsgrouped_step_sizesgrouped_state_stepssignsr|   rK   r   rL   
grad_signsr   s                                   @r.   _multi_tensor_rpropr   "  s`     6{aDDD >>&&((Z'H'J$ 
 v3
 
 
 	w WWsVttuv		w 
  BB	;7O ""$		 	d6lO<T&\>:T&\>:!$v,0CD"4<1EF ~~**,,1DQ1G1N1N#U\\#e%DC  3Q7 }>P ""=-@&
 	]M:.%U#

5;;twwqz7DAB

5;;twwqz8TBC

5;;twwqz1d;< 
 #*TWWQZ #+TWWQZ #$TWWQZ   	.6+I]: ,
 ]+s=)*A!""E!HKK11mA6FG +  /<<mddiikm
<J(:"	
E %B =s   P)single_tensor_fnr   c
                   [         R                  R                  5       (       d"  [        S U 5       5      (       d  [	        S5      eUc  [        XSS9u  pU(       a.  [         R                  R                  5       (       a  [	        S5      eU(       a*  [         R                  R                  5       (       d  [        nO[        nU" U UUUUU
UUUUUUU	S9  g)zhFunctional API that performs rprop algorithm computation.

See :class:`~torch.optim.Rprop` for details.
c              3   V   #    U  H  n[        U[        R                  5      v   M!     g 7frg   )r%   r=   r   )r   ts     r.   r   rprop.<locals>.<genexpr>  s!      5-8
1ell##[s   ')zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF)	use_fusedz6torch.jit.script not supported with foreach optimizers)r]   r^   r_   r`   r   r   r   rY   )
r=   rv   rw   r   rP   r   jitis_scriptingr   r   )r   rV   rW   r"   rX   r   r   r   r   rY   r]   r^   r_   r`   r   funcs                   r.   r   r     s    4 >>&&(( 5-85 2 2 ^
 	
 1e

 599))++STTuyy--//"###%r0   )NFFFF)__doc__typingr   r   r   r   r   r=   r   	optimizerr
   r   r   r   r   r   r   r   r   r   r   r   r   __all__r   r?   rl   r   r   r   rG   r0   r.   <module>r      s   9 5 5     " G
HI HX"F		 	 
 		 		 		 G1 lALA<A <A V	A
 fA A A A A A A A AHk
Lk
<k
 <k
 V	k

 fk
 k
 k
 k
 k
 k
 k
 k
 k
d  1EF # ;L;<; <; V	;
 f; d^; ; ; ; ; ; ;  !;" #; G;r0   