+
    ˜°³jõD  ã                   ó  € R t ^ RIHt ^ RIt^ RIHt ^RIHtHtHtH	t	H
t
HtHtHtHtHtHtHtHtHt RR.t ! R R]4      tRR	] R
] R]
 R] R] R2,           ]n         R R ltR R lt]	! ]R7      RR R ll4       tR# )z1Implementation for the Resilient backpropagation.)ÚcastN)ÚTensor)Ú_capturable_docÚ_default_to_fused_or_foreachÚ_differentiable_docÚ_disable_dynamo_if_unsupportedÚ_foreach_docÚ!_get_capturable_supported_devicesÚ_get_scalar_dtypeÚ_maximize_docÚ_params_docÚ
_to_scalarÚ_use_grad_for_differentiableÚ_view_as_realÚ	OptimizerÚParamsTÚRpropÚrpropc            	       ó|   a a€ ] tR t^t oRRRRRRRRR/V3R lV 3R lllltV 3R	 ltR
 t]RR l4       tRt	Vt
V ;t# )r   Ú
capturableFÚforeachNÚmaximizeÚdifferentiablec                ó˜   <€ V ^8„  d   QhRS[ RS[S[,          RS[S[S[3,          RS[S[S[3,          RS[RS[R,          RS[R	S[R
R/	# )é   ÚparamsÚlrÚetasÚ
step_sizesr   r   Nr   r   Úreturn)r   Úfloatr   ÚtupleÚbool)ÚformatÚ__classdict__s   "€ÚR/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/optim/rprop.pyÚ__annotate__ÚRprop.__annotate__   s   ø€ ÷ +ñ +áð+ñ ‘F�Nð+ñ ‘E™5�LÕ!ð	+ñ
 ™%¡˜,Õ'ð+ñ ð+ñ ˜•ð+ñ ð+ñ ð+ð 
ñ+ó    c               óh  <€ \        V\        4      '       d!   VP                  4       ^8w  d   \        R4      hRV8:  g   \        RV 24      hRV^ ,          u;8  d   Ru;8  d   V^,          8  g"   M \        RV^ ,           RV^,           24      hRVRVR	VR
VRVRVRV/p	\        S
V `  W4       R# )é   zTensor lr must be 1-elementg        zInvalid learning rate: ç      ð?zInvalid eta values: z, r   r   r   r   r   r   r   N)Ú
isinstancer   ÚnumelÚ
ValueErrorÚsuperÚ__init__)Úselfr   r   r   r   r   r   r   r   ÚdefaultsÚ	__class__s   &&&&&$$$$ €r%   r0   ÚRprop.__init__   sº   ø€ ô �bœ&×!Ò! b§h¡h£j°A¤oÜÐ:Ó;Ð;Ø�bŒyÜÐ6°r°dÐ;Ó<Ð<Ø�T˜!•WÖ,˜sÖ, T¨!¥WÖ,ÜÐ3°D¸µG°9¸B¸tÀA½w¸iÐHÓIÐIð �"Ø�DØ˜*Ø�wØ˜Ø˜nØ˜*ð
ˆô 	‰Ñ˜Ö*r(   c                ó„  <€ \         SV `  V4       V P                   EF  pVP                  R R4       VP                  RR4       VP                  RR4       VP                  RR4       VR,           FÅ  pV P                  P                  V. 4      p\        V4      ^ 8w  g   K1  \        P                  ! VR,          4      '       d   KV  \        VR,          4      pVR,          '       d,   \        P                  ! V\        4       VP                  R7      M\        P                  ! V\        4       R	7      VR&   KÇ  	  EK!  	  R# )
r   Nr   Fr   r   r   Ústep©ÚdtypeÚdevice©r8   )r/   Ú__setstate__Úparam_groupsÚ
setdefaultÚstateÚgetÚlenÚtorchÚ	is_tensorr    Útensorr
   r9   )r1   r>   ÚgroupÚpÚp_stateÚstep_valr3   s   &&    €r%   r;   ÚRprop.__setstate__=   sø   ø€ Ü‰Ñ˜UÔ#Ø×&Õ&ˆEØ×Ñ˜Y¨Ô-Ø×Ñ˜Z¨Ô/Ø×ÑÐ-¨uÔ5Ø×Ñ˜\¨5Ô1Ø˜8—_�_�ØŸ*™*Ÿ.™.¨¨BÓ/�Ü�w“< 1Ö$¬U¯_ª_¸WÀV½_×-MÔ-MÜ$ W¨V¥_Ó5�Hð
 ! ×.Ô.ô ŸšØ$Ô,=Ó,?ÈÏÉõô #Ÿ\š\¨(Ô:KÓ:MÔNð ˜F“Oô	 %ó 'r(   c           	     óô  € R pVR,           EFç  pVP                   f   K  V\        P                  ! V4      ,          pVP                  V4       VP                   p	V	P                  '       d   \        R4      hVP                  V	4       V P                  V,          p
\        V
4      ^ 8X  Ed    VR,          '       d,   \        P                  ! R\        4       VP                  R7      M\        P                  ! R\        4       R7      V
R&   \        P                  ! V\        P                  R7      V
R&   VP                  P                  '       d4   \        P                  ! V	\        VR	,          VR	,          4      4      V
R
&   M*\        P                  ! V	\!        VR	,          4      4      V
R
&   VP                  V
R,          4       VP                  V
R
,          4       VP                  V
R,          4       EKê  	  V# )Fr   z'Rprop does not support sparse gradientsr   r7   r:   r6   ©Úmemory_formatÚprevr   Ú	step_size© )ÚgradrA   Ú
is_complexÚappendÚ	is_sparseÚRuntimeErrorr>   r@   Úzerosr
   r9   Ú
zeros_likeÚpreserve_formatr8   Ú	full_likeÚcomplexr   )r1   rD   r   ÚgradsÚprevsr   Ústate_stepsÚhas_complexrE   rO   r>   s   &&&&&&&    r%   Ú_init_groupÚRprop._init_groupP   s�  € ØˆØ�x—‘ˆAØ�v‰vŠ~ÙØœ5×+Ò+¨AÓ.Õ.ˆKØ�M‰M˜!ÔØ—6‘6ˆDØ�~�~ˆ~Ü"Ð#LÓMÐMà�L‰L˜ÔØ—J‘J˜q•MˆEô �5‹z˜Q�ð ˜\×*Ô*ô —K’K Ô*;Ó*=ÀaÇhÁhÕOäŸš RÔ/@Ó/BÔCð �f‘ô !&× 0Ò 0°Ä%×BWÑBWÔ X��f‘Ø—7‘7×%×%Ð%ô */¯ªØœg e¨D¥k°5¸µ;Ó?ó*�E˜+Ò&ô */¯ª¸¼zÈ%ÐPTÍ+Ó?VÓ)W�E˜+Ñ&à�L‰L˜˜v�Ô'Ø×Ñ˜e KÕ0Ô1Ø×Ñ˜u V�}×-ñA !ðD Ðr(   c                ó°  € V P                  4        RpVe.   \        P                  ! 4       ;_uu_ 4        V! 4       pRRR4       V P                   Fo  p. p. p. p. p. pVR,          w  ršVR,          w  r¼VR,          pVR,          pV P	                  W4WVWx4      p\        VVVVVVVV	V
VVVR,          VR,          VR7       Kq  	  V#   + '       g   i     L‘; i)	z�Perform a single optimization step.

Args:
    closure (Callable, optional): A closure that reevaluates the model
        and returns the loss.
Nr   r   r   r   r   r   )	Ústep_size_minÚstep_size_maxÚetaminusÚetaplusr   r   r   r   r\   )Ú'_accelerator_graph_capture_health_checkrA   Úenable_gradr<   r]   r   )r1   ÚclosureÚlossrD   r   rY   rZ   r   r[   rb   rc   r`   ra   r   r   r\   s   &&              r%   r6   Ú
Rprop.stepv   sö   € ð 	×4Ñ4Ô6àˆØÒÜ×"Ò"×$Õ$Ù“y�÷ %ð ×&Ô&ˆEØ#%ˆFØ"$ˆEØ"$ˆEØ')ˆJØ(*ˆKà % f¥ÑˆHØ+0°Õ+>Ñ(ˆMØ˜IÕ&ˆGØ˜ZÕ(ˆHà×*Ñ*Ø˜u¨ZóˆKô ØØØØØØ+Ø+Ø!ØØØ!Ø$Ð%5Õ6Ø  Õ.Ø'÷ñ! 'ðB ˆ÷I %×$ús   ´CÃC	rN   )g{®Gáz„?)g      à?g333333ó?)g�íµ ÷Æ°>é2   ©N)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__r0   r;   r]   r   r6   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r3   r$   s   @@r%   r   r      s[   ù‡ € ñ+ð !ð+ð  $ð+ð ð+ð  %÷+ö +õ<ò&$ðL "ó/ó "÷/ð /r(   a¼
  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

    c                ó&  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\        R\        R	\        R
\        R\        R\        R\        RR/# ©r   r   rY   rZ   r   r[   r`   ra   rb   rc   r   r   r   r\   r   N©Úlistr   r    r"   )r#   s   "r%   r&   r&   ß   s¸   € ÷ Dñ DÜ”�LðDä”�<ðDô ”�<ðDô ”V•ð	Dô
 ”f•ðDô ðDô ðDô ðDô ðDô ðDô ðDô ðDô ðDð 
ñDr(   c                ó¨  € \        V 4       EFA  w  rÞW,          pV	'       g   TMV) pW-,          pW=,          pWM,          p\        P                  P                  4       '       gl   V
'       dd   \	        4       pVP
                  P                  VP
                  P                  8X  d   VP
                  P                  V9   g   \        R V R24      hV^,          p\        P                  ! V4      '       dY   \        P                  ! V4      p\        P                  ! V4      p\        P                  ! V4      p\        P                  ! V4      pV'       d/   VP                  VP                  4       4      P                  4       pMVP                  V4      P                  4       pV
'       d¤   VP                  \        P                  ! VP                  ^ 4      VV4      4       VP                  \        P                  ! VP!                  ^ 4      VV4      4       VP                  \        P                  ! VP#                  ^ 4      ^V4      4       M<VVVP                  ^ 4      &   VVVP!                  ^ 4      &   ^VVP#                  ^ 4      &   VP%                  V4      P'                  WV4       VP                  \        P(                  R7      pV
'       d8   VP                  \        P                  ! VP#                  V4      ^ V4      4       M^ VVP#                  V4      &   VP+                  VP                  4       VRR7       VP                  V4       EKD  	  R# )úIIf capturable=True, params and state_steps must be on supported devices: Ú.rJ   ©ÚvalueNéÿÿÿÿ)Ú	enumeraterA   ÚcompilerÚis_compilingr	   r9   ÚtypeÚAssertionErrorrP   Úview_as_realÚmulÚcloneÚsignÚcopy_ÚwhereÚgtÚltÚeqÚmul_Úclamp_rV   Úaddcmul_)r   rY   rZ   r   r[   r`   ra   rb   rc   r   r   r   r\   ÚiÚparamrO   rL   rM   r6   Úcapturable_supported_devicesr„   s   &&&&&$$$$$$$$        r%   Ú_single_tensor_rpropr�   ß   se  € ô  ˜f×%‰ˆØ�xˆß#‰t¨$¨ˆØ�xˆØ•Mˆ	Ø�~ˆô �~‰~×*Ñ*×,Ò,·Ü+LÓ+NÐ(à—‘×!Ñ! T§[¡[×%5Ñ%5Ô5Ø—L‘L×%Ñ%Ð)EÔEä$Ø_Ð`|Ð_}Ð}~Ðóð ð 	��	ˆä×Ò˜E×"Ò"Ü×%Ò% dÓ+ˆDÜ×%Ò% dÓ+ˆDÜ×&Ò& uÓ-ˆEÜ×*Ò*¨9Ó5ˆIßØ—8‘8˜DŸJ™J›LÓ)×.Ñ.Ó0‰Dà—8‘8˜D“>×&Ñ&Ó(ˆDçØ�J‰J”u—{’{ 4§7¡7¨1£:¨w¸Ó=Ô>Ø�J‰J”u—{’{ 4§7¡7¨1£:¨x¸Ó>Ô?Ø�J‰J”u—{’{ 4§7¡7¨1£:¨q°$Ó7Õ8à&ˆD�—‘˜“ÑØ'ˆD�—‘˜“ÑØ ˆD�—‘˜“Ñð 	�‰�tÓ×#Ñ# MÔAð �z‰z¬×(=Ñ(=ˆzÓ>ˆßØ�J‰J”u—{’{ 4§7¡7¨8Ó#4°a¸Ó>Õ?à&'ˆD�—‘˜Ó"Ñ#ð 	�‰�t—y‘y“{ I°RˆÔ8Ø�
‰
�4×ói &r(   c                ó&  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\        R\        R	\        R
\        R\        R\        R\        RR/# rs   rt   )r#   s   "r%   r&   r&   &  s¸   € ÷ o
ñ o
Ü”�Lðo
ä”�<ðo
ô ”�<ðo
ô ”V•ð	o
ô
 ”f•ðo
ô ðo
ô ðo
ô ðo
ô ðo
ô ðo
ô ðo
ô ðo
ô ðo
ð 
ño
r(   c          
      óø  a€ \        V 4      ^ 8X  d   R# V'       d   \        R4      h\        P                  P	                  4       '       gx   V
'       dp   \        4       o\        ;QJ d*    V3R l\        WRR7       4       F  '       d   K   RM	  RM! V3R l\        WRR7       4       4      '       g   \        RS R24      h\        P                  ! WW#V.4      pVP                  4        EF…  w  w  pppppp\        \        \        ,          V4      p\        \        \        ,          V4      p\        \        \        ,          V4      p\        \        \        ,          V4      p\        \        \        ,          V4      p\        P                  P	                  4       '       gJ   V^ ,          P                  '       d1   \        P                  ! V\        P                   ! R	R
R7      R	R7       M\        P                  ! V^4       V'       d   \#        VVVV4       \        P$                  ! VV4      pV	'       d   \        P&                  ! V4       \        P(                  ! VV4       V	'       d   \        P&                  ! V4       Tp\        P*                  ! V4       V
'       d­   V F¥  pVP-                  \        P.                  ! VP1                  ^ 4      VV4      4       VP-                  \        P.                  ! VP3                  ^ 4      VV4      4       VP-                  \        P.                  ! VP5                  ^ 4      ^V4      4       K§  	  MEV F?  pVVVP1                  ^ 4      &   VVVP3                  ^ 4      &   ^VVP5                  ^ 4      &   KA  	  \        P6                  ! VV4       V F  pVP9                  WV4       K  	  \        V4      p\;        \        V4      4       FN  pVV,          P-                  \        P.                  ! VV,          P5                  V4      ^ VV,          4      4       KP  	  ?V Uu. uF  pVP=                  4       NK  	  pp\        P>                  ! VVVRR7       EKˆ  	  R# u upi )é    Nz#_foreach ops don't support autogradc              3   óÄ   <"  € T FU  w  rVP                   P                  VP                   P                  8H  ;'       d    VP                   P                  S9   x € KW  	  R # 5irj   )r9   r   )Ú.0rE   r6   r�   s   &  €r%   Ú	<genexpr>Ú&_multi_tensor_rprop.<locals>.<genexpr>?  sT   øé € ð 
ñ A‘�ð �H‰H�M‰M˜TŸ[™[×-Ñ-Ñ-÷ >ð >Ø—‘—‘Ð!=Ñ=ô>ã@ùs
   ƒ:A ¾"A T)ÚstrictFrw   rx   r+   Úcpu)r9   )Úalphary   r{   ) r@   r€   rA   r}   r~   r	   ÚallÚzipr   Ú"_group_tensors_by_device_and_dtypeÚvaluesr   ru   r   Úis_cpuÚ_foreach_add_rC   r   Ú_foreach_mulÚ_foreach_neg_Ú_foreach_copy_Ú_foreach_sign_r…   r†   r‡   rˆ   r‰   Ú_foreach_mul_r‹   Úranger„   Ú_foreach_addcmul_) r   rY   rZ   r   r[   r`   ra   rb   rc   r   r   r   r\   Úgrouped_tensorsÚgrouped_params_Úgrouped_grads_Úgrouped_prevs_Úgrouped_step_sizes_Úgrouped_state_steps_Ú_Úgrouped_paramsÚgrouped_gradsÚgrouped_prevsÚgrouped_step_sizesÚgrouped_state_stepsÚsignsr„   rM   r�   rO   Ú
grad_signsr�   s    &&&&&$$$$$$$$                  @r%   Ú_multi_tensor_rpropr¶   &  sƒ  ø€ ô  ˆ6ƒ{�aÔÙçÜÐBÓCÐCô �>‰>×&Ñ&×(Ò(¯ZÜ'HÓ'JÐ$ß‹sô 
ô ˜v¸4Õ@ó
�s�sŠsô 
ô ˜v¸4Õ@ó
÷ 
ò 
ô
 !Ø[Ð\xÐ[yÐyzÐ{óð ô  ×BÒBØ	˜¨;Ð7ó€Oð ×"Ñ"×$ñ		ñ 	ØØØØØØÜœd¤6�l¨OÓ<ˆÜœT¤&�\¨>Ó:ˆÜœT¤&�\¨>Ó:ˆÜ!¤$¤v¥,Ð0CÓDÐÜ"¤4¬¥<Ð1EÓFÐô �~‰~×*Ñ*×,Ò,Ð1DÀQÕ1G×1N×1NÐ1NÜ×ÒØ#¤U§\¢\°#¸eÔ%DÈCöô ×ÒÐ 3°QÔ7÷ ÜØ ¨}Ð>Pôô ×"Ò" =°-Ó@ˆßÜ×Ò Ô&ô
 	×Ò˜]¨MÔ:ßÜ×Ò Ô.Ø%ˆä×Ò˜UÔ#ßÛ�Ø—
‘
œ5Ÿ;š; t§w¡w¨q£z°7¸DÓAÔBØ—
‘
œ5Ÿ;š; t§w¡w¨q£z°8¸TÓBÔCØ—
‘
œ5Ÿ;š; t§w¡w¨q£z°1°dÓ;Ö<ò ó
 �Ø#*��T—W‘W˜Q“ZÑ Ø#+��T—W‘W˜Q“ZÑ Ø#$��T—W‘W˜Q“ZÓ ñ ô 	×ÒÐ.°Ô6Û+ˆIØ×Ñ˜]Ö:ñ ,ô
 ˜]Ó+ˆÜ”s˜=Ó)Ö*ˆAØ˜!Õ×"Ñ"Ü—’˜E !�HŸK™K¨Ó1°1°mÀAÕ6FÓGöñ +ð ñ /<Ó<©m d�d—i‘i–k©mˆ
Ð<Ü×ÒØ˜JÐ(:À"÷	
ð 	
óE %ùòB =s   Ð;Q7)Úsingle_tensor_fnc                ó@  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R,          R\        R	\        R
\        R\        R\        R\        R\        R\        RR/# )r   r   rY   rZ   r   r[   r   Nr   r   r   r\   r`   ra   rb   rc   r   )ru   r   r"   r    )r#   s   "r%   r&   r&   �  s¶   € ÷ ;ñ ;Ü”�Lð;ä”�<ð;ô ”�<ð;ô ”V•ð	;ô
 ”f•ð;ô �D�[ð;ô ð;ô ð;ô ð;ô ð;ô ð;ô ð;ô  ð!;ô" ð#;ð$ 
ñ%;r(   c
               ó  € \         P                  P                  4       '       gF   \        ;QJ d    R V 4       F  '       d   K   RM	  RM! R V 4       4      '       g   \	        R4      hVf   \        WRR7      w  råV'       d0   \         P                  P                  4       '       d   \	        R4      hV'       d,   \         P                  P                  4       '       g   \        pM\        pV! V VVVVV
VVVVVVV	R7       R# )	zhFunctional API that performs rprop algorithm computation.

See :class:`~torch.optim.Rprop` for details.
c              3   óV   "  € T F  p\        V\        P                  4      x € K!  	  R # 5irj   )r,   rA   r   )r•   Úts   & r%   r–   Úrprop.<locals>.<genexpr>¶  s!   é € ð 5Ù-8¨Œ
�1”e—l‘l×#Ð#«[ùs   ‚')FTzPAPI has changed, `state_steps` argument must contain a list of singleton tensorsN)Ú	use_fusedz6torch.jit.script not supported with foreach optimizers)r`   ra   rb   rc   r   r   r   r\   )
rA   r}   r~   r›   rS   r   ÚjitÚis_scriptingr¶   r�   )r   rY   rZ   r   r[   r   r   r   r   r\   r`   ra   rb   rc   r®   Úfuncs   &&&&&&&&&&$$$$  r%   r   r   œ  sà   € ô4 �>‰>×&Ñ&×(Ò(·³ñ 5Ù-8ó5··²ñ 5Ù-8ó5÷ 2ò 2ô Ø^ó
ð 	
ð ‚Ü1Ø¨eô
‰
ˆ÷ ”5—9‘9×)Ñ)×+Ò+ÜÐSÓTÐTç”u—y‘y×-Ñ-×/Ò/Ü"‰ä#ˆáØØØØØØ#Ø#ØØØØØ%Ø÷r(   )NFFFF)Ú__doc__Útypingr   rA   r   Ú	optimizerr   r   r   r   r   r	   r
   r   r   r   r   r   r   r   Ú__all__r   r�   r¶   r   rN   r(   r%   Ú<module>rÅ      sÀ   ðá 8å ã Ý ÷÷ ÷ ÷ ð$ �GÐ
€ôHˆIô HðX!LðD	ð 
ˆð 	ð 
Ðð 	Ø	ˆð 	Ø	ˆð 	Ø	Ðð ðõE1ð „õlDõNo
ñl  Ð1EÔFö;ó Gò;r(   