¯wgdZddlZddlmZmZddlZddlmZddlmZm Z m Z ddl m Z m Z gdZeje j d Zed e j$d d d d d dde j&dej(deedej(dej(dedefdZedde j&fdZdZede j$d dddd ddd d&de j&dej(dedeedeed eej(d!ed"eed#eed$ej(fd%Zy)'aThis file exports ONNX ops for opset 17. Note [ONNX Operators that are added/updated in opset 17] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-17-of-the-default-onnx-operator-set New operators: BlackmanWindow DFT HammingWindow HannWindow LayerNormalization MelWeightMatrix STFT SequenceMap N)OptionalSequence)_C) _type_utilserrorssymbolic_helper) jit_utils registration) layer_normstftquantized_layer_norm)opsetzaten::layer_normvisfnoneginputnormalized_shapeweightbiaseps cudnn_enablect| }tjj|tjj}|j } t j|r*tj|| } |jd| }t j|r*tj|| } |jd| }|jd|||||S)NdtypeConstantvalue_tLayerNormalization) epsilon_faxis_i) lenr JitScalarType from_valueFLOATrr_is_nonetorchonesopzeros) rrrrrrraxis scalar_typer weight_value bias_values `/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/torch/onnx/symbolic_opset17.pyr r %s  ! !D++66 {((..K    E'zz"2%@ j,7%[[!1? ttJ t3 44    zquantized::layer_normc tj||\}}}}t||||||d} tj|| ||S)NF)rdequantize_helperr quantize_helper) rxrrrrop_scale op_zero_point_outputs r1r r IsL!221a8JAq!Q 1.c5 IF  * *1fh NNr2c*||z dz}||z |z }||fS)zuHelper function to compute the sizes of the edges (left and right) of a given window centered within an FFT size.)n_fft window_sizeleftrights r1_compute_edge_sizesrB[s+ K A %D DL; &E ;r2z aten::stftibr> hop_length win_lengthwindow normalizedonesidedreturn_complexreturnc |rtjd|||n|dz} |jdtj| tj } |jdtj|tj } |} t j| } | dk(rI|jd| |jdtjd gtj } n"| | d kDrtjd | d |t j|d }||r|n|}||k(s Jd|df||krqt||\}}|jdtj|}|jdtj|}|jd|||d }t j|r|r||kDrtjd|d|d|t||\}}tjtj|tj|tj|f}ntj|}|jd |k(sJ|jd|}|jd|tj j#| j%}|jd| | || ||rdnd }|jd|gd}| dk(rH|jd||jdtjd gtj }|rjtj&tj|| j)j+}|jd||jd|}|S)aAssociates `torch.stft` with the `STFT` ONNX operator. Note that torch.stft calls _VF.stft, without centering or padding options. Hence, this function does not contain these two arguments. See torch.stft source code for more info. Args: g: Graph to write the ONNX representation into input: Input tensor for the transformation n_fft: FFT size hop_length: Size of the hop. Defaults to `floot(n_fft // 4)` win_length: Size of the analysis window. Defaults to `n_fft` window: Analysis window. Defaults to a window of all ones normalized: Whether to return a normalized STFT onesided: Whether to return only half (+1) of the results, given the symmetry of the STFT return_complex: Whether to return the complex value (Note: Must be `False` or `None`) Returns: op: Operator for torch.stft associated with STFT (ONNX) z-STFT does not currently support complex types)msgvaluerrr Unsqueezerr<zcSTFT can only take inputs of 1 [signal] or 2 [batch, signal] dimensions. Current rank of signal is z, please reduce it.)dimzuAnalysis window size must equal `win_length` or `n_fft`. Please, set `win_length` or `n_fft` to match `window` size ()Concat)r#zWThe analysis window can't be longer than the size of the FFT. Please set `win_length` (z) to `n_fft` (z ) or less.Cast)to_iSTFT) onesided_i Transpose)rr<rP)perm_iSqueezeDiv)rSymbolicValueErrorr+r)tensorint64r_get_tensor_rank_get_tensor_dim_sizerBr,r(hstackr*shaperr%r& onnx_typesqrttyper)rrr>rErFrGrHrIrJframe_step_valueframe_step_constframe_length_constsignal signal_rankn_winwin_length_defaultr@rAleft_win right_win torch_windowresult sqrt_nffts r1r r csF''?u  &0%;z!ttELL)9MELLekkB F!226:Ka   DDU\\1#U[[%ID J    a''))4 5HJ    0 0Q ?E +5Z5**  KKP'QR T-  * 5=-eU;KD%ttJ D0AtBHZU1CDITT(HfiTJF' E!//00:|>%PZ\.eZ@KD% <<T"EJJz$:EKKrs"% ;;8 9"""<#=#=RH"#CsCf= 88sm HH  ((   >$D'(OO)O" Cc3S#sC !% $!%#%*BB 88B B B  B RXX  BBtnBTNBXXBDBr2