+
    ˜°³jžD  ã                   ó  € ^ 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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# )é    )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
_get_valueÚ_maximize_docÚ_params_docÚ
_to_scalarÚ_use_grad_for_differentiableÚ_view_as_realÚ	OptimizerÚParamsTÚAdamaxÚadamaxc                   óx   a a€ ] tR t^t o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maximizeFÚdifferentiableÚ
capturablec                óˆ   <€ V ^8„  d   QhRS[ RS[S[,          RS[S[S[3,          RS[RS[RS[R,          RS[R	S[R
S[RR/
# )é   ÚparamsÚlrÚbetasÚepsÚweight_decayÚforeachNr   r   r   Úreturn)r   Úfloatr   ÚtupleÚbool)ÚformatÚ__classdict__s   "€ÚS/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/optim/adamax.pyÚ__annotate__ÚAdamax.__annotate__   s€   ø€ ÷ $+ñ $+áð$+ñ ‘F�Nð$+ñ ‘U™E�\Õ"ð	$+ñ
 ð$+ñ ð$+ñ ˜•ð$+ñ ð$+ñ ð$+ñ ð$+ð 
ñ$+ó    c               óè  <€ \        V\        4      '       d!   VP                  4       ^8w  d   \        R4      hRV8:  g   \        RV 24      hRV8:  g   \        RV 24      hRV^ ,          u;8:  d   R8  g   M \        RV^ ,           24      hRV^,          u;8:  d   R8  g   M \        RV^,           24      hRV8:  g   \        RV 24      hR	V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-elementç        zInvalid learning rate: zInvalid epsilon value: ç      ð?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: r   r   r   r    r!   r   r   r   N)Ú
isinstancer   ÚnumelÚ
ValueErrorÚsuperÚ__init__)Úselfr   r   r   r   r    r!   r   r   r   ÚdefaultsÚ	__class__s   &&&&&&&$$$ €r(   r4   ÚAdamax.__init__   s	  ø€ ô �bœ&×!Ò! b§h¡h£j°A¤oÜÐ:Ó;Ð;Ø�bŒyÜÐ6°r°dÐ;Ó<Ð<Ø�cŒzÜÐ6°s°eÐ<Ó=Ð=Ø�e˜A•hÖ$ Ö$ÜÐBÀ5ÈÅ8À*ÐMÓNÐNØ�e˜A•hÖ$ Ö$ÜÐBÀ5ÈÅ8À*ÐMÓNÐNØ�lÔ"ÜÐ;¸L¸>ÐJÓKÐKð �"Ø�UØ�3Ø˜LØ�wØ˜Ø˜nØ˜*ð	
ˆô 	‰Ñ˜Ö*r+   c                ó„  <€ \         SV `  V4       V P                   EF  pVP                  R R4       VP                  RR4       VP                  RR4       VP                  RR4       VR,           FÅ  pV P                  P                  V. 4      p\        V4      ^ 8w  g   K1  \        P                  ! VR,          4      '       d   KV  \        VR,          4      pVR,          '       d,   \        P                  ! V\        4       VP                  R7      M\        P                  ! V\        4       R	7      VR&   KÇ  	  EK!  	  R# )
r!   Nr   Fr   r   r   Ústep©ÚdtypeÚdevice©r<   )r3   Ú__setstate__Úparam_groupsÚ
setdefaultÚstateÚgetÚlenÚtorchÚ	is_tensorr#   Útensorr   r=   )r5   rB   ÚgroupÚpÚp_stateÚstep_valr7   s   &&    €r(   r?   ÚAdamax.__setstate__D   sø   ø€ Ü‰Ñ˜UÔ#Ø×&Õ&ˆEØ×Ñ˜Y¨Ô-Ø×Ñ˜Z¨Ô/Ø×ÑÐ-¨uÔ5Ø×Ñ˜\¨5Ô1Ø˜8—_�_�ØŸ*™*Ÿ.™.¨¨BÓ/�Ü�w“< 1Ö$¬U¯_ª_¸WÀV½_×-MÔ-MÜ$ W¨V¥_Ó5�Hð
 ! ×.Ô.ô ŸšØ$Ô,=Ó,?ÈÏÉõô #Ÿ\š\¨(Ô:KÓ:MÔNð ˜F“Oô	 %ó 'r+   c                ób  € R pVR,           EFž  pVP                   f   K  V\        P                  ! V4      ,          pVP                  V4       VP                   P                  '       d   \        R4      hVP                  VP                   4       V P                  V,          p	\        V	4      ^ 8X  d°   VR,          '       d,   \        P                  ! R\        4       VP                  R7      M\        P                  ! R\        4       R7      V	R&   \        P                  ! V\        P                  R7      V	R	&   \        P                  ! V\        P                  R7      V	R
&   VP                  V	R	,          4       VP                  V	R
,          4       VP                  V	R,          4       EK¡  	  V# )Fr   z(Adamax does not support sparse gradientsr   r;   r.   r>   r:   )Úmemory_formatÚexp_avgÚexp_inf© )ÚgradrE   Ú
is_complexÚappendÚ	is_sparseÚRuntimeErrorrB   rD   Úzerosr   r=   rG   Ú
zeros_likeÚpreserve_format)
r5   rH   Úparams_with_gradÚgradsÚexp_avgsÚexp_infsÚstate_stepsÚhas_complexrI   rB   s
   &&&&&&&   r(   Ú_init_groupÚAdamax._init_groupW   sR  € ð ˆØ�x—‘ˆAØ�v‰vŠ~ÙØœ5×+Ò+¨AÓ.Õ.ˆKØ×#Ñ# AÔ&Ø�v‰v××ÐÜ"Ð#MÓNÐNØ�L‰L˜Ÿ™Ô à—J‘J˜q•MˆEô �5‹z˜QŒð ˜\×*Ô*ô —K’K Ô*;Ó*=ÀaÇhÁhÕOäŸš cÔ1BÓ1DÔEð �f‘ô
 $)×#3Ò#3Ø¤U×%:Ñ%:ô$��iÑ ô $)×#3Ò#3Ø¤U×%:Ñ%:ô$��iÑ ð �O‰O˜E )Õ,Ô-Ø�O‰O˜E )Õ,Ô-Ø×Ñ˜u V�}×-ñ7 !ð: Ðr+   c                óÚ  € V P                  4        RpVe.   \        P                  ! 4       ;_uu_ 4        V! 4       pRRR4       V P                   F„  p. p. p. p. p. pVR,          w  ršVR,          pVR,          pVR,          pVR,          pVR,          pVR,          pVR	,          pV P	                  W4WVWx4      p\        VVVVVVV	V
VVVVVVVR
7       K†  	  V#   + '       g   i     L¦; i)z‘Performs a single optimization step.

Args:
    closure (Callable, optional): A closure that reevaluates the model
        and returns the loss.
Nr   r   r   r    r!   r   r   r   )
r   Úbeta1Úbeta2r   r    r!   r   r   r   r_   )Ú'_accelerator_graph_capture_health_checkrE   Úenable_gradr@   r`   r   )r5   ÚclosureÚlossrH   rZ   r[   r\   r]   r^   rc   rd   r   r   r    r!   r   r   r   r_   s   &&                 r(   r:   ÚAdamax.stepz   s  € ð 	×4Ñ4Ô6àˆØÒÜ×"Ò"×$Õ$Ù“y�÷ %ð ×&Ô&ˆEØ-/ÐØ"$ˆEØ%'ˆHØ%'ˆHØ(*ˆKà  �>‰LˆEØ˜•,ˆCØ�t•ˆBØ  Õ0ˆLØ˜IÕ&ˆGØ˜ZÕ(ˆHØ"Ð#3Õ4ˆNØ˜|Õ,ˆJà×*Ñ*Ø¨¸(óˆKô Ø ØØØØØØØØØ)ØØ!Ø-Ø%Ø'÷ñ) 'ðL ˆ÷S %×$ús   ´CÃC*	rQ   )gü©ñÒMb`?)gÍÌÌÌÌÌì?g+‡ÙÎ÷ï?g:Œ0âŽyE>r   N©N)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__r4   r?   r`   r   r:   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r7   r'   s   @@r(   r   r      sR   ù‡ € ñ$+ð ð$+ð  %ð$+ð !÷$+ö $+õLò&!ðF "ó4ó "÷4ð 4r+   aÁ  Implements Adamax algorithm (a variant of Adam based on infinity norm).

    .. math::
       \begin{aligned}
            &\rule{110mm}{0.4pt}                                                                 \\
            &\textbf{input}      : \gamma \text{ (lr)}, \beta_1, \beta_2
                \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
                \: \lambda \text{ (weight decay)},                                                \\
            &\hspace{13mm}    \epsilon \text{ (epsilon)}                                          \\
            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
                u_0 \leftarrow 0 \text{ ( infinity norm)}                                 \\[-1.ex]
            &\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}if \: \lambda \neq 0                                                    \\
            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
            &\hspace{5mm}m_t      \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t               \\
            &\hspace{5mm}u_t      \leftarrow   \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon)   \\
            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_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 `Adam: A Method for Stochastic Optimization`_.
    z
    Args:
        a›  
        lr (float, Tensor, optional): learning rate (default: 2e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        z	
        zd

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980

    c                óH  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\        R\        R	\        \        ,          R
\        R\        R\        R\        R\        RR/# ©r   r   r[   r\   r]   r^   r   rc   rd   r   r    r   r   r   r_   r"   N©Úlistr   r#   r%   )r&   s   "r(   r)   r)   â   sÇ   € ÷ M9ñ M9Ü”�LðM9ä”�<ðM9ô ”6�lðM9ô ”6�lð	M9ô
 ”f•ðM9ô 
ðM9ô ðM9ô ðM9ô 	”�ðM9ô ðM9ô ðM9ô ðM9ô ðM9ô ðM9ð  
ñ!M9r+   c       	   	      ó  € \         P                  P                  4       '       g   \        V4      p\	        V 4       EFÈ  w  rïW,          pV
'       g   TMV) pW.,          pW>,          pWN,          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V	^ 8w  d   VP                  WùR7      p\         P                  ! V4      '       dY   \         P                  ! V4      p\         P                  ! V4      p\         P                  ! V4      p\         P                  ! V4      pVP                  V^V,
          4       V'       gG   \         P                  ! VP!                  V4      VP#                  4       P%                  V4      VR7       M‹\         P&                  ! VP!                  V4      P)                  ^ 4      VP#                  4       P%                  V4      P+                  ^ 4      .^ 4      pVP-                  \         P.                  ! V^ RR7      4       V'       d@   VV,          ^,
          pVP1                  V4       VV,          pVP3                  VV4       EK‘  ^V\5        V4      ,          ,
          pVV,          pVP3                  VVV) R7       EKË  	  R# )úIIf capturable=True, params and state_steps must be on supported devices: Ú.©Úalpha)ÚoutF)Úkeepdim)ÚvalueN)rE   ÚjitÚis_scriptingr   Ú	enumerateÚcompilerÚis_compilingr
   r=   ÚtypeÚAssertionErrorÚaddrS   Úview_as_realÚlerp_ÚmaximumÚmul_ÚabsÚadd_ÚcatÚ	unsqueezeÚ
unsqueeze_Úcopy_ÚamaxÚdiv_Úaddcdiv_r   )r   r[   r\   r]   r^   r   rc   rd   r   r    r   r   r   r_   ÚiÚparamrR   rO   rP   Ústep_tÚcapturable_supported_devicesÚnorm_bufÚneg_bias_correctionÚdenomÚbias_correctionÚclrs   &&&&&$$$$$$$$$            r(   Ú_single_tensor_adamaxrœ   â   sN  € ô" �9‰9×!Ñ!×#Ò#Ü˜‹^ˆä˜f×%‰ˆØ�xˆß#‰t¨$¨ˆØ•+ˆØ•+ˆØ•ˆô �~‰~×*Ñ*×,Ò,·Ü+LÓ+NÐ(à—‘×!Ñ! V§]¡]×%7Ñ%7Ô7Ø—L‘L×%Ñ%Ð)EÔEä$Ø_Ð`|Ð_}Ð}~Ðóð ð
 	�!�ˆà˜1ÔØ—8‘8˜E�8Ó6ˆDä×Ò˜E×"Ò"Ü×&Ò& uÓ-ˆEÜ×%Ò% dÓ+ˆDÜ×(Ò(¨Ó1ˆGÜ×(Ò(¨Ó1ˆGð 	�‰�d˜A �IÔ&çÜ�MŠMØ—‘˜UÓ#Ø—‘“
—‘ Ó$Øöô —y’yØ—‘˜eÓ$×.Ñ.¨qÓ1°4·8±8³:·?±?À3Ó3G×3RÑ3RÐSTÓ3UÐVØóˆHð �M‰Mœ%Ÿ*š* X¨q¸%Ô@ÔAçð #(¨¥-°!Õ"3ÐØ×$Ñ$ RÔ(ØÐ1Õ1ˆEØ�N‰N˜7 E×*à %¬:°fÓ+=Õ"=Õ=ˆOØ�Õ&ˆCà�N‰N˜7 G°C°4ˆN×8ós &r+   c                óH  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\        R\        R	\        \        ,          R
\        R\        R\        R\        R\        RR/# rs   rt   )r&   s   "r(   r)   r)   2  sÇ   € ÷ sñ sÜ”�Lðsä”�<ðsô ”6�lðsô ”6�lð	sô
 ”f•ðsô 
ðsô ðsô ðsô 	”�ðsô ðsô ðsô ðsô ðsô ðsð  
ñ!sr+   c       	   	      ó>  a€ V'       d   \        R 4      h\        V 4      ^ 8X  d   R# \        P                  P	                  4       '       gz   V'       dr   \        RR7      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\        V4      p\        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V'       d   \        VVVV4       V
'       d   \        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	^ 8w  d<   V
'       d   \        P$                  ! VVV	R7       M\        P(                  ! VVV	R7      p\        P*                  ! VV^V,
          4       \        P,                  ! VV4       V
'       g   V	^ 8X  d   \        P.                  ! V4      pM\        P0                  ! V4       \        P$                  ! VV4       \        P2                  ! VV4       V'       dx   \        P4                  ! VV4      p\        P6                  ! V^4       \        P8                  ! VV4       \        P:                  ! VV4      p\        P<                  ! VVV4       EK­  V Uu. uF  p^V\?        V4      ,          ,
          NK  	  ppV Uu. uF  p\?        V4      V,          R,          NK  	  pp\        P<                  ! VVVV4       EK  	  R# u upi u upi )z#_foreach ops don't support autogradNF)Úsupports_xlac              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   )r=   rƒ   )Ú.0rI   r:   r–   s   &  €r(   Ú	<genexpr>Ú'_multi_tensor_adamax.<locals>.<genexpr>N  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strictrw   rx   r/   Úcpu)r=   ry   éÿÿÿÿ) r„   rD   rE   r�   r‚   r
   ÚallÚzipr   r   Ú"_group_tensors_by_device_and_dtypeÚvaluesr   ru   r   r   Ú_foreach_negÚis_cpuÚ_foreach_add_rG   Ú_foreach_addÚ_foreach_lerp_Ú_foreach_mul_Ú_foreach_absÚ_foreach_abs_Ú_foreach_maximum_Ú_foreach_powÚ_foreach_sub_Ú_foreach_div_Ú_foreach_mulÚ_foreach_addcdiv_r   ) r   r[   r\   r]   r^   r   rc   rd   r   r    r   r   r   r_   Úgrouped_tensorsÚgrouped_params_Úgrouped_grads_Úgrouped_exp_avgs_Úgrouped_exp_infs_Úgrouped_state_steps_Ú_Úgrouped_paramsÚgrouped_gradsÚgrouped_exp_avgsÚgrouped_exp_infsÚgrouped_state_stepsÚbias_correctionsr™   r:   ÚbcÚ	step_sizer–   s    &&&&&$$$$$$$$$                 @r(   Ú_multi_tensor_adamaxrÈ   2  sf  ø€ ÷" ÜÐBÓCÐCä
ˆ6ƒ{�aÔÙô �>‰>×&Ñ&×(Ò(¯ZÜ'HØô(
Ð$÷ ‹sô 
ô ˜v¸4Õ@ó
�s�sŠsô 
ô ˜v¸4Õ@ó
÷ 
ò 
ô
 !Ø[Ð\xÐ[yÐyzÐ{óð ô 
�B‹€Bä×BÒBØ	˜¨KÐ8ó€Oð ×"Ñ"×$ñ		ñ 	ØØØØØØÜœd¤6�l¨OÓ<ˆÜœT¤&�\¨>Ó:ˆÜ¤¤V¥Ð.?Ó@ÐÜ¤¤V¥Ð.?Ó@ÐÜ"¤4¬¥<Ð1EÓFÐçÜØ Ð/?ÐAQô÷ Ü!×.Ò.¨}Ó=ˆMô �~‰~×*Ñ*×,Ò,Ð1DÀQÕ1G×1N×1NÐ1NÜ×ÒØ#¤U§\¢\°#¸eÔ%DÈCöô ×ÒÐ 3°QÔ7à˜1Ôßä×#Ò# M°>ÈÖVä %× 2Ò 2Ø! >¸ô!�ô
 	×ÒÐ-¨}¸aÀ%½iÔHô 	×ÒÐ,¨eÔ4÷ ˜L¨AÔ-Ü!×.Ò.¨}Ó=‰Mä×Ò Ô.ä×Ò˜M¨3Ô/Ü×ÒÐ 0°-Ô@÷ Ü$×1Ò1°%Ð9LÓMÐä×ÒÐ 0°!Ô4Ü×ÒÐ 0°"Ô5ä×&Ò&Ð'7Ð9IÓJˆEÜ×#Ò# NÐ4DÀe×Lñ ;Nó Ù:M°$��EœZ¨Ó-Õ-×-Ð-Ñ:Mð ð  ñ ?OÓOÑ>N¸œ* R›.¨2Õ-°×3Ð3Ñ>NˆIÐOÜ×#Ò#ØÐ 0Ð2BÀI÷óC %ùòz ùò Ps   Î)"PÏ"P)Úsingle_tensor_fnc                 ób  € V ^8„  d   Qh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!   Nr   r   r   r_   r   rc   rd   r   r    r"   )ru   r   r%   r#   )r&   s   "r(   r)   r)   ©  sÄ   € ÷ <ñ <Ü”�Lð<ä”�<ð<ô ”6�lð<ô ”6�lð	<ô
 ”f•ð<ô �D�[ð<ô 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  rõV'       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	VR7       R# )	zjFunctional API that performs adamax algorithm computation.

See :class:`~torch.optim.Adamax` for details.
c              3   óV   "  € T F  p\        V\        P                  4      x € K!  	  R # 5irj   )r0   rE   r   )r¡   Úts   & r(   r¢   Úadamax.<locals>.<genexpr>Â  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   rc   rd   r   r    r   r   r_   r   )
rE   r�   r‚   r§   rV   r   r~   r   rÈ   rœ   )r   r[   r\   r]   r^   r!   r   r   r   r_   r   rc   rd   r   r    r¿   Úfuncs   &&&&&&&&&&$$$$$  r(   r   r   ¨  sã   € 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Ø÷r+   )NFFFF)Útypingr   rE   r   Ú	optimizerr   r   r   r   r	   r
   r   r   r   r   r   r   r   r   r   Ú__all__r   Ú__doc__rœ   rÈ   r   rQ   r+   r(   Ú<module>rÕ      s½   ðå ã Ý ÷÷ ÷ ÷ ñ ð& �XÐ
€ôRˆYô Rðlð4	à	ˆð 	ð 
ˆð 	Ø	ˆð 	Ø	Ðð 	Ø	Ðð ðõ5+ð „õ`M9õ`sñl  Ð1FÔGö<ó Hò<r+   