hZ%SSKrSSKrSSKJr SSKJr SSKJr SSKJ r J r J r J r SSK r SSK JrJr SSKJr SS KJrJr SS KJrJr SS KJr S S KJrJrJr S SKJr S SK J!r!J"r"J#r# /SQr$\"SS55r%"SS\%5r&"SS\%5r'"SS\RP5r)"SS\RP5r*"SS\RP5r+S\\ \&\'4S\,S\ \-S \ \S!\.S"\ S#\+4S$jr/S%\0S"\ S#\1\\ \&\'4\ \-44S&jr2S'\0r3\4\0\ 4\5S('0\3ES)S*S+.Er60\3ES,S-S+.Er7"S.S/\5r8"S0S1\5r9"S2S3\5r:"S4S5\5r;"S6S7\5r<"S8S9\5r="S:S;\5r>"S<S=\5r?"S>S?\5r@"S@SA\5rA"SBSC\5rB\"5\#"SD\8R4SE9SSFSG.S \ \8S!\.S"\ S#\+4SHjj55rD\"5\#"SD\9R4SE9SSFSG.S \ \9S!\.S"\ S#\+4SIjj55rE\"5\#"SD\:R4SE9SSFSG.S \ \:S!\.S"\ S#\+4SJjj55rF\"5\#"SD\;R4SE9SSFSG.S \ \;S!\.S"\ S#\+4SKjj55rG\"5\#"SD\R4SE9SSFSG.S \ \>S!\.S"\ S#\+4SNjj55rJ\"5\#"SD\?R4SE9SSFSG.S \ \?S!\.S"\ S#\+4SOjj55rK\"5\#"SD\@R4SE9SSFSG.S \ \@S!\.S"\ S#\+4SPjj55rL\"5\#"SD\AR4SE9SSFSG.S \ \AS!\.S"\ S#\+4SQjj55rM\"5\#"SD\BR4SE9SSFSG.S \ \BS!\.S"\ S#\+4SRjj55rNg)SN)Sequence) dataclass)partial)AnyCallableOptionalUnion)nnTensor)StochasticDepth)Conv2dNormActivationSqueezeExcitation)ImageClassificationInterpolationMode)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_make_divisible_ovewrite_named_paramhandle_legacy_interface) EfficientNetEfficientNet_B0_WeightsEfficientNet_B1_WeightsEfficientNet_B2_WeightsEfficientNet_B3_WeightsEfficientNet_B4_WeightsEfficientNet_B5_WeightsEfficientNet_B6_WeightsEfficientNet_B7_WeightsEfficientNet_V2_S_WeightsEfficientNet_V2_M_WeightsEfficientNet_V2_L_Weightsefficientnet_b0efficientnet_b1efficientnet_b2efficientnet_b3efficientnet_b4efficientnet_b5efficientnet_b6efficientnet_b7efficientnet_v2_sefficientnet_v2_mefficientnet_v2_lc \rSrSr%\\S'\\S'\\S'\\S'\\S'\\S'\S\R4\S '\ SS \S \S \ \S\4Sjj5r Sr g ) _MBConvConfig/ expand_ratiokernelstrideinput_channels out_channels num_layers.blockNchannels width_mult min_valuereturnc [X-SU5$)N)r)r<r=r>s W/var/www/auris/envauris/lib/python3.13/site-packages/torchvision/models/efficientnet.pyadjust_channels_MBConvConfig.adjust_channels9sx4aCCN)__name__ __module__ __qualname____firstlineno__float__annotations__intrr Module staticmethodrrC__static_attributes__rFrErBr3r3/so K KO CN ##D#D5DXc]D^aDDrEr3c^\rSrSrSS\S\S\S\S\S\S \S \S \\S \R4S S4U4Sjjjr \ S\S \4Sj5r Sr U=r$) MBConvConfig>Nr5r6r7r8r9r:r= depth_multr;.r?c >URXG5nURXW5nURXh5nU c[n [T U]XX4XVU 5 grG)rC adjust_depthMBConvsuper__init__) selfr5r6r7r8r9r:r=rUr; __class__s rBrZMBConvConfig.__init__@sS--nI++LE &&z> =E v|afgrEcD[[R"X-55$rG)rNmathceil)r:rUs rBrWMBConvConfig.adjust_depthSs499Z4566rErF)?rbN)rHrIrJrKrLrNrrr rOrZrPrWrQ __classcell__r\s@rBrSrS>s 48hhh h  h  hhhhbii01h hh&77%77rErScv^\rSrSrSS\S\S\S\S\S\S \\S \R4S S4U4S jjjr S r U=r $)FusedMBConvConfigXNr5r6r7r8r9r:r;.r?c <>Uc[n[TU] XX4XVU5 grG) FusedMBConvrYrZ) r[r5r6r7r8r9r:r;r\s rBrZFusedMBConvConfig.__init__Zs# =E v|afgrErFrG) rHrIrJrKrLrNrrr rOrZrQrcrds@rBrfrfXs|59 h h h h  h  h hbii01 h  h hrErfc ^\rSrSr\4S\S\S\S\R4S\S\R4SS4 U4S jjjr S \ S\ 4S jr S r U=r$) rXicnfstochastic_depth_prob norm_layer.se_layerr?Nc X>[T U]5 SURs=::aS::d O [S5eURS:H=(a URUR :HUl/n[RnURURUR5nXqR:wa&UR[URUSUUS95 UR[UUURURUUUS95 [SURS-5nURU"Xx[[RSS9S 95 UR[XqR SUSS95 [R "U6Ul[%US 5UlUR Ulg) Nrr illegal stride value kernel_sizeroactivation_layer)rtr7groupsroruT)inplace) activationrow)rYrZr7 ValueErrorr8r9use_res_connectr SiLUrCr5appendrr6maxr Sequentialr;r stochastic_depth) r[rmrnrorplayersruexpanded_channelssqueeze_channelsr\s rBrZMBConv.__init__js~ SZZ$1$34 4"zzQY33E3EIYIY3Y"$77 //0B0BCDTDTU  2 2 2 MM$&&% !)%5    !!JJzz(%!1  q#"4"4"9: h0wWYW^W^hlOmno  !#3#3zlp  ]]F+ /0Eu M,,rEinputctURU5nUR(aURU5nX!- nU$rGr;r|rr[rresults rBforwardMBConv.forward5E"   **62F OF rEr;r9rr|)rHrIrJrKrrSrLrr rOrZr rrQrcrds@rBrXrXisu .? 8- 8- %8-S"))^, 8- 3 >* 8-  8-8-tVrErXcl^\rSrSrS\S\S\S\R4SS4U4Sjjr S \ S\ 4S jr S r U=r $) rirmrnro.r?Nc  >[TU]5 SURs=::aS::d O [S5eURS:H=(a URUR :HUl/n[RnURURUR5nXaR:waaUR[URUURURUUS95 UR[XaR SUSS95 OEUR[URUR URURUUS95 [R"U6Ul[!US5UlUR Ulg)Nrr rrrtr7rorursrz)rYrZr7r{r8r9r|r r}rCr5r~rr6rr;r r)r[rmrnrorrurr\s rBrZFusedMBConv.__init__sT SZZ$1$34 4"zzQY33E3EIYIY3Y"$77//0B0BCDTDTU  2 2 2 MM$&&% # ::)%5   MM$%'7'7QS]pt  MM$&&$$ # ::)%5  ]]F+ /0Eu M,,rErctURU5nUR(aURU5nX!- nU$rGrrs rBrFusedMBConv.forwardrrEr)rHrIrJrKrfrLrr rOrZr rrQrcrds@rBririsT2- 2- %2-S"))^, 2-  2-hVrEric^\rSrSrSS\\\\4S\S\S\ S\ \ S\ R4S \ \ S S4U4S jjjrS \S \4S jrS \S \4SjrSrU=r$)rNinverted_residual_settingdropoutrn num_classesro. last_channelr?c >[TU]5 [U5 U(d [S5e[ U[ 5(a/[ UVs/sHn[ U[5PM sn5(d [S5eUc[Rn/nUSRn UR[SU SSU[RS95 [SU55n Sn UHn /n [!U R"5Hpn[$R$"U 5nU (aUR&Ul S UlU[+U 5-U - nU RUR-UUU55 U S - n Mr UR[R."U 65 M US R&nUbUOS U-nUR[UUS U[RS 95 [R."U6Ul[R2"S 5Ul[R."[R6"US S9[R8"UU55UlUR=5GHn[ U[R>5(ab[R@RCURDSS9 URFb+[R@RIURF5 MM[ U[R[RJ45(aU[R@RMURD5 [R@RIURF5 GM [ U[R85(dGM+S[NRP"URR5- n[R@RUURDU*U5 [R@RIURF5 GM gs snf)a EfficientNet V1 and V2 main class Args: inverted_residual_setting (Sequence[Union[MBConvConfig, FusedMBConvConfig]]): Network structure dropout (float): The droupout probability stochastic_depth_prob (float): The stochastic depth probability num_classes (int): Number of classes norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use last_channel (int): The number of channels on the penultimate layer z1The inverted_residual_setting should not be emptyz:The inverted_residual_setting should be List[MBConvConfig]Nrr rc38# UHoRv M g7frG)r:).0rms rB (EfficientNet.__init__..s U;TC;TsrrwrsT)prxfan_out)moderb)+rYrZrr{ isinstancerallr3 TypeErrorr BatchNorm2dr8r~rr}sumranger:copyr9r7rLr;rfeaturesAdaptiveAvgPool2davgpoolDropoutLinear classifiermodulesConv2dinitkaiming_normal_weightbiaszeros_ GroupNormones_r_sqrt out_featuresuniform_)r[rrrnrrorsrfirstconv_output_channelstotal_stage_blocksstage_block_idrmstage_ block_cnfsd_problastconv_input_channelslastconv_output_channelsm init_ranger\s rBrZEfficientNet.__init__s ( D!(PQ Q 0( ; ;;TU;TaZ=1;TUVVXY Y  J"$%>a$@$O$O! ,!AR\oqovov  ! 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Aq#sB / Aq#sB / Aq#sA .% !  % 2224&9::rEr _COMMON_META)rrzUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1)min_sizerecipe)!r zUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2c h\rSrSr\"S\"\SS\RS90\ ESSSS S .0S S S S.ES9r \ r Sr g)rizJhttps://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pthrr crop_size resize_size interpolationidP ImageNet-1Kg?5^IlS@g5^I bW@zacc@1zacc@5gNbX9?g~jts4@1These weights are ported from the original paper. num_params_metrics_ops _file_size_docsurl transformsrrFN rHrIrJrKrrrrBICUBIC_COMMON_META_V1 IMAGENET1K_V1DEFAULTrQrFrErBrra X 3CO`OhOh     !##   L  M(GrErc \rSrSr\"S\"\SS\RS90\ ESSSS S .0S S S S.ES9r \"S\"\SS\RS90\ ESSSSSS .0S SSS.ES9r \ r Srg)rizJhttps://download.pytorch.org/models/efficientnet_b1_rwightman-bac287d4.pthrr ivrg +S@gClW@rgCl?gM">@rrrz@https://download.pytorch.org/models/efficientnet_b1-c27df63c.pthzOhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuninggʡS@gƻW@gA`">@$ These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. )rrrrrrrFN)rHrIrJrKrrrrrrrBILINEAR IMAGENET1K_V2rrQrFrErBrrs X 3CO`OhOh     !##   L  M( N 3CO`OiOi   !g##    M0GrErc h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)rrzJhttps://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pthi r irgx&T@gp= W@rg rh?gʡEA@rrrrFNrrFrErBrrr rErc h\rSrSr\"S\"\SS\RS90\ ESSSS S .0S S S S.ES9r \ r Sr g)rizJhttps://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pthi,rr irgnT@g~jtX@rgZd;?gd;OG@rrrrFNrrFrErBrra X 3CO`OhOh     "##   L  M(GrErc h\rSrSr\"S\"\SS\RS90\ ESSSS S .0S S S S.ES9r \ r Sr g)r i0zJhttps://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pthi|rr i0!'rgjtT@gt&X@rg~jt@gKR@rrrrFNrrFrErBr r 0r)rEr c h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)r!iHzJhttps://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pthir irg#~jT@gx&1(X@rgx&1$@gK7]@rrrrFNrrFrErBr!r!Ha X 3CO`OhOh     "##  !L  M(GrEr!c h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)r"i`zJhttps://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pthir irgnU@gv:X@rg rh3@g$d@rrrrFNrrFrErBr"r"`r,rEr"c h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)r#ixzJhttps://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pthiXr icrg+U@g'1:X@rgsh|B@go@rrrrFNrrFrErBr#r#xr,rEr#c h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)r$izBhttps://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pthrr i8nGrg;OU@gx&18X@rgZd @gVT@r$rrrFN rHrIrJrKrrrrr%_COMMON_META_V2rrrQrFrErBr$r$se P +44    "##    M4GrEr$c h\rSrSr\"S\"\SS\RS90\ ESSSSS .0S S S S .ES9r \ r Sr g)r%izBhttps://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pthr i:rgI +GU@gDlIX@rgE8@gQj@r$rrrFNr0rFrErBr%r%se P +44    "##    M4GrEr%c l\rSrSr\"S\"\SS\RSSS90\ ESSSS S .0S S S S.ES9r \ r Sr g)r&izBhttps://download.pytorch.org/models/efficientnet_v2_l-59c71312.pthr3)?r5r5)r r rmeanstdiHfrgʡEsU@gOnrX@rg ףp= L@gI +i|@rrrrFN) rHrIrJrKrrrrrr1rrrQrFrErBr&r&si P +33       ###  !L  M0GrEr& pretrained)rT)rrc [RU5n[SSSS9up4[X2R SS5X@U40UD6$)aEfficientNet B0 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B0_Weights :members: r'rbrrr)rverifyrrrrrrrrs rBr'r'Q.&,,W5G.@AR_bor.s+ !::i#=|V^ bh rEc [RU5n[SSSS9up4[X2R SS5X@U40UD6$)aEfficientNet B1 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B1_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B1_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B1_Weights :members: r(rb皙?rrr)rr:rrrr;s rBr(r(r<rEc [RU5n[SSSS9up4[X2R SS5X@U40UD6$)aEfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B2_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B2_Weights :members: r)r>333333?rr333333?)rr:rrrr;s rBr)r)'r<rEc [RU5n[SSSS9up4[UUR SS5UUU40UD6$)aEfficientNet B3 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B3_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B3_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B3_Weights :members: r*r@ffffff?rrrA)rr:rrrr;s rBr*r*FZ.&,,W5G.@AR_bor.s+ ! 9c"     rEc [RU5n[SSSS9up4[UUR SS5UUU40UD6$)aEfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B4_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B4_Weights :members: r+rC?rr皙?)r r:rrrr;s rBr+r+jrDrEc [RU5n[SSSS9up4[UUR SS5UUU4S[ [ RSS S 90UD6$) aEfficientNet B5 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B5_Weights :members: r,g?g@rrrGroMbP?{Gz?epsmomentum)r!r:rrrrr rr;s rBr,r,q.&,,W5G.@AR_bor.s+ ! 9c"   2>>utD    rEc [RU5n[SSSS9up4[UUR SS5UUU4S[ [ RSS S 90UD6$) aEfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B6_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B6_Weights :members: r-rFg@rrr5rorIrJrK)r"r:rrrrr rr;s rBr-r-rNrEc [RU5n[SSSS9up4[UUR SS5UUU4S[ [ RSS S 90UD6$) aEfficientNet B7 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B7_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B7_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B7_Weights :members: r.g@g@rrr5rorIrJrK)r#r:rrrrr rr;s rBr.r.rNrEc [RU5n[S5up4[UUR SS5UUU4S[ [ RSS90UD6$)a Constructs an EfficientNetV2-S architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_S_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights :members: r/rrrorIrL)r$r:rrrrr rr;s rBr/r/i0(..w7G.@AT.U+ ! 9c"   2>>u5    rEc [RU5n[S5up4[UUR SS5UUU4S[ [ RSS90UD6$)a Constructs an EfficientNetV2-M architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_M_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights :members: r0rrArorIrR)r%r:rrrrr rr;s rBr0r0#rSrEc [RU5n[S5up4[UUR SS5UUU4S[ [ RSS90UD6$)a Constructs an EfficientNetV2-L architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_L_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_L_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_L_Weights :members: r1rrGrorIrR)r&r:rrrrr rr;s rBr1r1IrSrE)Orr_collections.abcr dataclassesr functoolsrtypingrrrr rr r torchvision.opsr ops.miscrrtransforms._presetsrrutilsr_apirrr_metar_utilsrrr__all__r3rSrfrOrXrirrLrNboolrstrtuplerrdictrMrr1rrrrr r!r"r#r$r%r&rr'r(r)r*r+r,r-r.r/r0r1rFrErBrfs $!11 +>H'66'SS 6  D D  D7=74h h"@RYY@F:")):zo%299o%d'l