o 闦i@sZdZddlZddlmZmZddlZddlmZddlmZm Z m Z ddl m Z m Z gdZeje jdd Zed e d d d d d dde jdejdeedejdejdedefddZedde jfddZddZede d dddd ddd    ! d,de jdejd"ed#eed$eed%eejd&ed'eed(eed)ejfd*d+ZdS)-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_enablec Cst| }tj|tjj}|} t|r%tj || d} |j d| d}t|r8tj || d} |j d| d}|j d|||||dS)NdtypeConstantvalue_tLayerNormalization) epsilon_faxis_i) lenr JitScalarType from_valueFLOATrr_is_nonetorchonesopzeros) rrrrrrraxis scalar_typer weight_value bias_valuer/Y/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/onnx/symbolic_opset17.pyr %s&   r zquantized::layer_normc Cs8t||\}}}}t||||||d} t|| ||S)NF)rdequantize_helperr quantize_helper) rxrrrrop_scale op_zero_point_outputr/r/r0r Is r cCs ||d}|||}||fS)zuHelper function to compute the sizes of the edges (left and right) of a given window centered within an FFT size.r/)n_fft window_sizeleftrightr/r/r0_compute_edge_sizes[s  r=z aten::stftibFTr9 hop_length win_lengthwindow normalizedonesidedreturn_complexreturnc  Cs|r tjd|d|dur|n|d} |jdtj| tjdd} |jdtj|tjdd} |} t| } | dkrL|d | |jdtjd gtjdd} n| dusT| d kr_tjd | d |dtj|d d}|dur|rn|n|}||ks}Jd|df||krt ||\}}|jdt |d}|jdt |d}|jd|||d d}t |r|r||krtjd|d|d|dt ||\}}t t |t |t |f}nt |}|jd |ksJ|jd|d}|jd|tj| d}|jd| | || |dus |r dnd d}|jd|gdd}| dkr2|d||jdtjd gtjdd}|rOttj|| d}|d||jd|d}|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)msgvalueNrrr Unsqueezerr8zcSTFT 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)rr8rJ)perm_iSqueezeDiv)rSymbolicValueErrorr)r'tensorint64r_get_tensor_rank_get_tensor_dim_sizer=r*r&hstackr(shaperr#r$ onnx_typesqrttyper)rrr9r@rArBrCrDrEframe_step_valueframe_step_constframe_length_constsignal signal_rankn_winwin_length_defaultr;r<left_win right_win torch_windowresult sqrt_nfftr/r/r0r cs#      r )NNNFTF)__doc__ functoolstypingrrr'r torch.onnxrrrtorch.onnx._internalrr __all__partial onnx_symbolic_onnx_symbolic parse_args GraphContextValueintfloatboolr r r=r r/r/r/r0sx "