oMjoSSKrSSKJs Jr SSKJr SSKJr SSKJrJ r \"5(aSSK r \R\R\R\R\RS.rS\S\R"4S jr"S S \R"5r"S S \R"5r "SS\R"5r"SS\R"5r"SS\R"5r"SS\R"5rg)N)nn) deprecate)is_torch_npu_availableis_torch_version)swishsilumishgelureluact_fnreturnc UR5nU[;a[U"5$[SUS[[R 5535e)zHelper function to get activation function from string. Args: act_fn (str): Name of activation function. Returns: nn.Module: Activation function. zactivation function z not found in ACT2FN mapping )lowerACT2CLS ValueErrorlistkeys)r s ڪC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\diffusers/models/activations.pyget_activationr$sO\\^F v  /x7TUYZaZfZfZhUiTjkllcf^\rSrSrSrU4SjrS\RS\R4SjrSr U=r $)FP32SiLU5z@ SiLU activation function with input upcasted to torch.float32. c">[TU]5 gN)super__init__)self __class__s rrFP32SiLU.__init__:s rinputsrcz[R"UR5SS9RUR5$)NF)inplace)Fr floattodtype)rr"s rforwardFP32SiLU.forward=s(vvfllne477 EEr) __name__ __module__ __qualname____firstlineno____doc__rtorchTensorr)__static_attributes__ __classcell__r s@rrr5s1FellFu||FFrrc ^\rSrSrSrS S\S\S\S\4U4SjjjrS\ RS \ R4S jr S r S r U=r$)GELUAa GELU activation function with tanh approximation support with `approximate="tanh"`. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. dim_indim_out approximatebiascb>[TU]5 [R"XUS9UlX0lgNr<)rrrLinearprojr;)rr9r:r;r<r s rr GELU.__init__Ls& IIfD9 &rgaterc6URRS:Xab[SS5(aQ[R"UR [ RS9URS9R URS9$[R"XRS9$)Nmps<2.0.0r()r;) devicetyperr%r r'r1float32r;r(rrCs rr GELU.geluQsq ;;  u $)9#w)G)G66$'' '6DDTDTUXX_c_i_iXj jvvd(8(899rcJURU5nURU5nU$r)rAr r hidden_statess rr) GELU.forwardWs$ -0  -0 r)r;rA)noneT)r,r-r.r/r0intstrboolrr1r2r r)r3r4r5s@rr7r7AsT's'S's'SW'' ::%,,: rr7c^\rSrSrSrS S\S\S\4U4SjjjrS\RS\R4S jr S r S r U=r $) GEGLU]a6 A [variant](https://huggingface.co/papers/2002.05202) of the gated linear unit activation function. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. r9r:r<c\>[TU]5 [R"XS-US9UlgNrr?rrrr@rArr9r:r<r s rrGEGLU.__init__gs$ IIfk= rrCrcURRS:XaY[SS5(aH[R"UR [ RS95R URS9$[R"U5$)NrErFrGrH) rIrJrr%r r'r1rKr(rLs rr GEGLU.geluks_ ;;  u $)9#w)G)G66$'' '67:::L Lvvd|rc [U5S:dURSS5bSn[SSU5 URU5n[ 5(a[ R "USSS9S$URSSS 9upXRU5-$) NrscalezThe `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.z1.0.0)dimr;rrd) lengetrrAr torch_npu npu_gegluchunkr )rrPargskwargsdeprecation_messagerCs rr) GEGLU.forwardqs t9q=FJJw5A#U  gw(; < -0 ! # #&&}"!LQO O"/"5"5aR"5"@ M 99T?2 2rrAT)r,r-r.r/r0rSrUrr1r2r r)r3r4r5s@rrWrW]sL>s>S>>>%,, 3 3rrWcF^\rSrSrSrS S\S\S\4U4SjjjrSrSr U=r $) SwiGLU~au A [variant](https://huggingface.co/papers/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU` but uses SiLU / Swish instead of GeLU. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. r9r:r<c>[TU]5 [R"XS-US9Ul[R "5UlgrZ)rrrr@rASiLU activationr\s rrSwiGLU.__init__s1 IIfk= '')rcnURU5nURSSS9upXRU5-$)Nrrbre)rArjrv)rrPrCs rr)SwiGLU.forwards: -0 +11!1< t444rrvrArp) r,r-r.r/r0rSrUrr)r3r4r5s@rrrrr~s1$s$S$$$ 55rrrcz^\rSrSrSrS S\S\S\4U4SjjjrS\RS\R4S jr S r U=r $) ApproximateGELUal The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this [paper](https://huggingface.co/papers/1606.08415). Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. r9r:r<cV>[TU]5 [R"XUS9Ulgr>r[r\s rrApproximateGELU.__init__s  IIfD9 rxrc\URU5nU[R"SU-5-$)NgZd;?)rAr1sigmoid)rrs rr)ApproximateGELU.forwards' IIaL5==+++rrorp) r,r-r.r/r0rSrUrr1r2r)r3r4r5s@rr|r|sG:s:S:::,,%,,,,rr|c F^\rSrSrS S\S\S\S\4U4SjjjrSrSr U=r $) LinearActivationr9r:r<rvcv>[TU]5 [R"XUS9Ul[ U5Ulgr>)rrrr@rArrv)rr9r:r<rvr s rrLinearActivation.__init__s, IIfD9 (4rcFURU5nURU5$r)rArvrOs rr)LinearActivation.forwards -0 }--rrz)Tr ) r,r-r.r/rSrUrTrr)r3r4r5s@rrrs45s5S55QT55 ..rr)r1torch.nn.functionalr functionalr%utilsrutils.import_utilsrrrhruMishr7ReLUrrTModulerrrWrrr|rr+rrrs  IWW GG GG GG GG  m3m299m" Fryy F29983BII3B5RYY5.,bii,( .ryy .r