i ) SrSSKrSSKJr SSKrSSKrSSKrSSKr SSK J r SSK J r SSKJr \R "\5rS\\ S\\\\44S jrS \\S\\4S jrSS jrS rS\\R6-S\\R6-S\S\4SjrS\\\R6-\\R6-4S\\\R6-\\R6-4S\S\S\\R:S44 Sjrg)z??$7 $B! z*  *  $ $T *  %J7 C)Oo5 **    7IIy--H'' A7:<7S 7StDAID1 $7S  "**    HHNN II;j) + ))+  EEJJ ]]O00 1 ZZL      'KN [>0 s G2GcUR[R"U5R[R R [R"U5U(aUOUS-S9n[R"5nUR5 UR[RRXR/U/U5 g)aInsert a TOSA reshape operator using the v1.0 semantics. Args: tosa_graph (Any): Graph builder used to emit TOSA operators. input_name (str): Name of the tensor that should be reshaped. new_shape (list[int]): Target tensor shape. output_name (str): Name assigned to the reshaped tensor. shape_name_override (str): Optional override for the shape constant name. _shaper!N) r'nparrayrr(r)r*r+ReshapeAttributer-r.RESHAPEr") tosa_graph input_namer; output_nameshape_name_overriderrBs rr%r%s    !!  $7 [8=S  E  % % 'D  ZZ   c [UVs/sHo UPM sn5n[UVs/sH&n[U[R5(aSOUPM( sn5n[ U5$s snfs snf)a{Reorder a shape tuple into TOSA layout while resolving symints. Args: shape (Sequence[int | torch.SymInt]): Original tensor shape, possibly containing ``torch.SymInt``. dim_order (Sequence[int]): Desired dimension order for the output shape. Returns: list[int]: List containing the reordered dimensions where symbolic values become ``-1``. )tuple isinstancetorchSymIntr)r dim_orderr reordereddremoved_symintss r tosa_shaperXsjY7YcSzY78I;DE9az!U\\** 19EO   8 Fs A'-A, input_size output_size resize_mode align_cornersc6U(a5[U[5(a[U[5(d [S5e[U[R5(aUR R OUn[U[R5(aUR R OUnU(aUS:a US:aUS- nOUnU(aUS:a US:aUS- nOUnXE- n[R"U5R5(d[S[U5-S-5e[R"XE5nSU-U-nSU-U-n[UR55n[UR55nU(aSnO US-US-- nXQS- -X@S- -- U-n [R"U 5R5(d[S[U 5-S-5e[[R"U 5R55n XEX4$)aCompute resize coefficients for a single spatial dimension. Args: input_size (int | torch.SymInt): Input size for the axis, possibly symbolic. output_size (int | torch.SymInt): Output size for the axis, possibly symbolic. resize_mode (int): Target resize mode defined by TOSA. align_corners (bool): Whether the resize should align the corner pixels. Returns: tuple[int, int, int, int]: Numerator, denominator, offset, and border terms encoded as integers. Raises: RuntimeError: If symbolic shapes are used with ``align_corners`` or if the computed ratio or border is not constant. z9We do not support align_corners=True for symbolic shapes.r z"Resize requires a constant ratio: z is not constant!rz#Resize requires a constant border: ) rQintrrRrSr1_exprsympysympify is_constantstrgcdevalf) rYrZr[r\scale_nscale_dratioreoffsetborders rget_resize_parameters_1drls6:s++Z S5Q5QK  ",J !E!E :#-[%,,"G"G [aK!O/aK!Oq.  E ==  + + - - 03u: =@S S   ))G %C'kS G'kS G'--/"G'--/"GA1 ,a (71n+E E NF == , , . . 1CK ?BU U  v&,,. /F V ++rM input_size_xyoutput_size_xy.c [USUSX#5n[USUSX#5n[[[R[ XE555$)ayCalculate 2D resize parameters for TOSA emission. Args: input_size_xy (tuple[int | torch.SymInt, int | torch.SymInt]): Height and width of the input tensor. output_size_xy (tuple[int | torch.SymInt, int | torch.SymInt]): Height and width of the output tensor. resize_mode (int): TOSA resize mode used for coefficient generation. align_corners (bool): Whether to align corner pixels between input and output. Returns: tuple[torch.IntTensor, ...]: Four-element tuple of tensors describing the scale numerator, scale denominator, offset, and border for Y and X dimensions. rr )rlrPmaprR IntTensorr&)rmrnr[r\y_paramsx_paramss rget_resize_parametersrtsY0(a.+[H(a.+[H U__c(&=> ??rM))__doc__loggingtypingrnumpyrErarRtosa_serializerr($executorch.backends.arm.tosa.mappingrtorch._subclasses.fake_tensorrtorch.fxr getLogger__name__loggerrrPboolr_rr5r%rXrSrlrqrtrMrrsN C D4   8 $&3z"&3 4c?&3RDd4jDT#YDPIK>!.L,ell"L,u||#L,L, L,^@u||+S5<<-??@@# ,cELL.@@A@@ @  5??C  @rM