[ThSSKJr SSKrSSKJr SrSrSr"SS\RR5r SSS\S4S jr S S jr g) )OptionalN)_NnapiSerializerc^\rSrSr%Sr\\RRR\ S'\ \R\ S'\ \R\ S'S\RRS\RS\ \RS\ \S \ \S \S \4U4S jjr\R$R&S \ \R4Sj5rS \ \RS\ \R4SjrSrU=r$) NnapiModulezTorch Module that wraps an NNAPI Compilation. This module handles preparing the weights, initializing the NNAPI TorchBind object, and adjusting the memory formats of all inputs and outputs. compweights out_templatesshape_compute_module ser_model inp_mem_fmts out_mem_fmtscompilation_preferencerelax_f32_to_f16c>[TU]5 XlX lX0lX@lXPl/UlSUlX`l Xpl gN) super__init__r rr rrr r rr) selfr rr rrrr __class__s U/var/www/auris/envauris/lib/python3.13/site-packages/torch/backends/_nnapi/prepare.pyrNnapiModule.__init__sG $8!" (( &<# 0argscURbeURRURU5UlUR Vs/sHo"R 5PM snUl[RRR5nURURUR URUR5 X0lgs snfr)r r preparerr r contiguoustorchclasses_nnapi Compilationinit2rr)rrwr s rinitNnapiModule.init0syy   !66>>t~~tT04 = 1  = }}##//1 NN LL  ' '  ! !   >s Creturnc |URcURU5 URnUceURVs/sHn[R"U5PM nn[ U5[ UR 5:Xde/n[[ U55HnUR UnUS:Xa#URXR55 M;US:Xa5URXRSSSS5R55 Mv[S5e URXT5 [ U5[ UR5:Xde[[ UR55HCnURUnUS;aMUS:XaXFRSSSS5XF'M:[S5e U$s snf)NrrrzInvalid mem_fmt)rr)r r&r r empty_likelenrrangeappendrpermute ValueErrorrunr)rrr outouts fixed_argsidxfmts rforwardNnapiModule.forward?s 99  IIdOyy151C1CD1C#  %1CD4yC 1 12222 T#C##C(Cax!!$)"6"6"89!!$)"3"3Aq!Q"?"J"J"LM !233$ "4yC 1 12222T//01C##C(Cf} I--aAq9  !2332 5Es F9) r rrrr rrr r )__name__ __module__ __qualname____firstlineno____doc__rr r!r"r#__annotations__listTensornnModuleintboolrjitexportr&r7__static_attributes__ __classcell__rs@rrrs 5==''33 44 %,,  %%1#hhoo1<<1ell# 1 3i 1 3i 1!$11* YY ell+  D.4 3ErrFc [XX#U5unnn n n n [UUU U U UU5n "SS[RR5nU"U 5n[R R U5nSRS[[U5555nU S:aSnO!SRS[U 555nURS US US US 35 U$) Nc,^\rSrSrSrU4SjrSrU=r$)5convert_model_to_nnapi..NnapiInterfaceWrapperaNNAPI list-ifying and de-list-ifying wrapper. NNAPI always expects a list of inputs and provides a list of outputs. This module allows us to accept inputs as separate arguments. It returns results as either a single tensor or tuple, matching the original module. c.>[TU]5 Xlgr)rrmod)rrOrs rr>convert_model_to_nnapi..NnapiInterfaceWrapper.__init__s G  Hr)rO)r9r:r;r<r=rrGrHrIs@rNnapiInterfaceWrapperrLs   rrQz, c3,# UH nSU3v M g7f)arg_N.0r5s r )convert_model_to_nnapi..sD1C#4u1Csrz retvals[0]c3.# UH nSUS3v M g7f)zretvals[z], NrTrUs rrWrXsN:M3XcU#.:Mszdef forward(self, z): retvals = self.mod([z]) return  ) process_for_nnapirr rArBrEscriptjoinr-r,define)modelinputs serializer return_shapesuse_int16_for_qint16rrr ser_model_tensor used_weightsrr retval_count nnapi_modelrQwrapper_model_py wrapper_modelarg_listret_exprs rconvert_model_to_nnapirmas  z2F  K  -[9II$$%56MyyDs6{1CDDHa77N% :MNN XJ'##+*-Zr # rc@[RRU5n[U[R5(aU/nU=(d [ SUS9nUR XU5unnnnn n [R"U[RS9n "SS[RR5n [RRU "55n S/U Vs/sH nSUS3PM sn-nU RSRU55 U U UUUU 4$s snf) N)configrd)dtypec\rSrSrSrSrg)-process_for_nnapi..ShapeComputeModulezCode-gen-ed module for tensor shape computation. module.prepare will mutate ser_model according to the computed operand shapes, based on the shapes of args. Returns a list of output templates. rTN)r9r:r;r<r=rGrTrrShapeComputeModulerrs rrtz\def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]: z r[rY)r rEfreeze isinstancer@rserialize_modeltensorint32rArBr]r_r^)r`rarbrcrdrrfrrshape_compute_linesrgrertr linereal_shape_compute_liness rr\r\s II  U #E&%,,''/*> J ""5-@||IU[[A UXX__ !99++,>,@Ag %89%8T4vR%89 :(@ AB    :sD)NNF) typingrr torch.backends._nnapi.serializerr ANEURALNETWORKS_PREFER_LOW_POWER)ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER&ANEURALNETWORKS_PREFER_SUSTAINED_SPEEDrArBrrmr\rTrrrs^ =$% ,-))*&P%((//PlA8xNS+r