shA: SrSSKJrJrJr SSKrSSKJs Jr SSK J r /SQr "SS5r "SS 5r"S S 5r\"S \ S 9rSS\S\S\S\S\\S\4 SjjrSS\S\S\4Sjjrg)z=Spectral Normalization from https://arxiv.org/abs/1802.05957.)AnyOptionalTypeVarN)Module) SpectralNorm SpectralNormLoadStateDictPreHookSpectralNormStateDictHook spectral_normremove_spectral_normc 4\rSrSr%Sr\\S'\\S'\\S'\\S'\\S'SS\S\S\S\S S 4 S jjr S\ RS \ R4S jr S \ S\S \ R4SjrS \ S S 4SjrS \ S\S S 4SjrSr\S \ S\S\S\S\S S4 Sj5rSrg )r_versionnamedimn_power_iterationsepsweightreturnNc\XlX0lUS::a[SU35eX lX@lg)NrzGExpected n_power_iterations to be positive, but got n_power_iterations=)rr ValueErrorrr)selfrrrrs p/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py__init__SpectralNorm.__init__"s@   "**<)=? #5c"UnURS:waVUR"UR/[UR55Vs/sHo3UR:wdMUPM snQ76nURS5nUR US5$s snf)Nr)rpermuterangesizereshape)rr weight_matdheights rreshape_weight_to_matrix%SpectralNorm.reshape_weight_to_matrix3s 88q=#++',Z^^-='>P'>!txx-A'>PJ#!!&"--Qs B B moduledo_power_iterationc @[XRS-5n[XRS-5n[XRS-5nURU5nU(a[R"5 [ UR 5H|n[R"[R"UR5U5SURUS9n[R"[R"Xe5SURUS9nM~ UR S:a:UR[RS9nUR[RS9nSSS5 [R"U[R"Xe55nX8- nU$!,(df  N?=f)N_orig_u_vr)rrout) memory_format)getattrrr&torchno_gradr rF normalizemvtrclonecontiguous_formatdot) rr(r)ruvr#_sigmas rcompute_weightSpectralNorm.compute_weight=s0<W!45 FII, - FII, -226: t667A 3aA EHHZ$;VWXA8**Q.e.E.EFAe.E.EFA! !UXXj45 !!s 6C F Fc[R"5 URUSS9nSSS5 [XR5 [XRS-5 [XRS-5 [XRS-5 UR UR[R RWR555 g!,(df  N=f)NFr)r,r-r+) r1r2r>delattrrregister_parameternn Parameterdetach)rr(rs rremoveSpectralNorm.removess ]]_((E(JF " D() D() G+,!!$))UXX-?-? -PQ _s C C"inputsc `[UURURXRS95 g)NrA)setattrrr>training)rr(rIs r__call__SpectralNorm.__call__|s)  II   ??  K rc n[RRUR5R U5R 5UR5UR S5/5RS5nURU[R"U[R"X55- 5$)Nr) r1linalg multi_dotr6mmpinverse unsqueezesqueezemul_r9r5)rr#r: target_sigmar;s r_solve_v_and_rescale!SpectralNorm._solve_v_and_rescales LL " " \\^  z * 3 3 5z||~q{{ST~ V '!* vvlUYYq%((:2I%JJKKrcURR5H8n[U[5(dMURU:XdM,[ SU35e [XX45nUR UnUc[SUS35e[U[RRR5(a [S5e[R"5 URU5nUR5up[R "UR#U 5R%SS5SUR&S9n [R "UR#U 5R%SS5SUR&S9n SSS5 [)XR5 UR+URS-U5 [-XRUR.5 UR1URS -W 5 UR1URS -W 5 UR3U5 UR5[7U55 UR9[;U55 U$!,(df  N=f) Nz>Cannot register two spectral_norm hooks on the same parameter z/`SpectralNorm` cannot be applied as parameter `z ` is NonezThe module passed to `SpectralNorm` can't have uninitialized parameters. Make sure to run the dummy forward before applying spectral normalizationrr)rrr+r,r-)_forward_pre_hooksvalues isinstancerr RuntimeError _parametersrr1rD parameterUninitializedParameterr2r&r!r3r4 new_emptynormal_rrBrCrKdataregister_bufferregister_forward_pre_hook_register_state_dict_hookr "_register_load_state_dict_pre_hookr) r(rrrrhookfnrr#hwr:r;s rapplySpectralNorm.applys--446D$ --$))t2C"TUYTZ[7 $C =##D) >A$yQ  fehh00GG H H\  ]]_44VrGrrMrX staticmethodrm__static_attributes__rrrrs Hc I H J"#       ".u||. .4V44%,,4lRVRR v s t L++!+7:+AD+KP+ ++rrc,\rSrSrSSjrSSjrSrg)rNcXlgNrjrrjs rr)SpectralNormLoadStateDictPreHook.__init__rc^^URnURS05RURS-S5n U bU S:aX(R-mU c"[UU4SjS55(aTT;agSn SH,n TU -n U T;dMSn U(dMUR U 5 M. U (ag[ R "5 TTS -n TRT5nX- R5nURU 5nTTS -nURUUU5nUTTS -'SSS5 gg!,(df  g=f) Nr .versionrc34># UH nTU-T;v M g7frr|).0s state_dict weight_keys r .sT>S Q*4>Ss)r+r,r-F)r+r,Tr+r,r-) rjgetrallappendr1r2popmeanr&rX)rrprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsrjversionhas_missing_keyssuffixkey weight_origrr=r#r:r;rs ` @rrM)SpectralNormLoadStateDictPreHook.__call__sFWW $$_b9== GGj $  ?gk'')JT>STTTj0 $ - 6)j('+$v$++C0 .  (g)=> # 3$-33588E zD01++J5A01 :,-!)*(!s A D55 ErrNrprqrrrsrrMr{r|rrrrs)2 )2rrc(\rSrSrSSjrSSjrSrg)r NcXlgrrrs rr"SpectralNormStateDictHook.__init__rrcSU;a0US'URRS-nXTS;a[SU35eURRUSU'g)Nr rz-Unexpected key in metadata['spectral_norm']: )rjrr^r)rr(rrrrs rrM"SpectralNormStateDictHook.__call__s] . 0.0N? +ggllZ' 1 1!NseTU U/3ww/?/?',rrrrr|rrr r s @rr T_module)boundr(rrrrrcUca[U[RR[RR[RR 45(aSnOSn[ RXX$U5 U$)aApply spectral normalization to a parameter in the given module. .. math:: \mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})}, \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2} Spectral normalization stabilizes the training of discriminators (critics) in Generative Adversarial Networks (GANs) by rescaling the weight tensor with spectral norm :math:`\sigma` of the weight matrix calculated using power iteration method. If the dimension of the weight tensor is greater than 2, it is reshaped to 2D in power iteration method to get spectral norm. This is implemented via a hook that calculates spectral norm and rescales weight before every :meth:`~Module.forward` call. See `Spectral Normalization for Generative Adversarial Networks`_ . .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957 Args: module (nn.Module): containing module name (str, optional): name of weight parameter n_power_iterations (int, optional): number of power iterations to calculate spectral norm eps (float, optional): epsilon for numerical stability in calculating norms dim (int, optional): dimension corresponding to number of outputs, the default is ``0``, except for modules that are instances of ConvTranspose{1,2,3}d, when it is ``1`` Returns: The original module with the spectral norm hook .. note:: This function has been reimplemented as :func:`torch.nn.utils.parametrizations.spectral_norm` using the new parametrization functionality in :func:`torch.nn.utils.parametrize.register_parametrization`. Please use the newer version. This function will be deprecated in a future version of PyTorch. Example:: >>> m = spectral_norm(nn.Linear(20, 40)) >>> m Linear(in_features=20, out_features=40, bias=True) >>> m.weight_u.size() torch.Size([40]) rr)r]r1rDConvTranspose1dConvTranspose2dConvTranspose3drrm)r(rrrrs rr r sjp {  ((((((   CCv%7cB MrcURR5HLup#[U[5(dMURU:XdM.UR U5 URU O [ SUSU35eURR5HEup#[U[5(dMURRU:XdM8URU O URR5HFup#[U[5(dMURRU:XdM8URU U$ U$)zRemove the spectral normalization reparameterization from a module. Args: module (Module): containing module name (str, optional): name of weight parameter Example: >>> m = spectral_norm(nn.Linear(40, 10)) >>> remove_spectral_norm(m) zspectral_norm of 'z' not found in ) r[itemsr]rrrGr_state_dict_hooksr rj_load_state_dict_pre_hooksr)r(rkris rr r Qs,,224 dL ) )dii4.? KK ))!,  5 -dV?6(KLL++113 d5 6 6477<<4;O((+ 4 44::< d< = =$'',,RVBV11!4  M = Mr)rrroN)r)__doc__typingrrrr1torch.nn.functionalrD functionalr3torch.nn.modulesr__all__rrr rrvrtrwr r r|rrrsC)) # eeT6262v @ @ :V ,  E E EE  E # E  EPHr