o ii@sLdZddlZddlmZmZddlZddlmZddlmZm Z m Z ddl m Z m Z mZddgZejejd d Zed e d d d d ddde jdejdeedejdejdedefddZddZede d dddd ddde j      d(de jdejded eed!eed"eejd#ed$eed%eed&ejfd'dZdS))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) _beartype jit_utils registration layer_normstft)Zopsetzaten::layer_normvisfnoneginputnormalized_shapeweightbiaseps cudnn_enablecCs t| }|jd|||||dS)NZLayerNormalization)Z epsilon_faxis_i)lenop)rrrrrrrZaxisrj/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/torch/onnx/symbolic_opset17.pyr "s 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_fftZ window_sizeleftrightrrr_compute_edge_sizes<s  r"z aten::stftibFTr hop_length win_lengthwindow normalizedonesidedreturn_complexreturnc  Cs|r tjd|d|dur|n|d} |jdtj| tjdd} |jdtj|tjdd} |} t| } | dkrL|d | |jdtjd gtjdd} n| d kr[tjd | d |dtj|d d}|dur|rj|n|}||ksyJd|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|rdnd d}|jd|gdd}| dkr.|d||jdtjd gtjdd}|rKttj|| 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)msgvalueNConstant)dtype)Zvalue_tZ UnsqueezerrzcSTFT 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 ()ZConcat)rzWThe analysis window can't be longer than the size of the FFT. Please set `win_length` (z) to `n_fft` (z ) or less.ZCast)Zto_iZSTFT)Z onesided_iZ Transpose)rrr1)Zperm_iZSqueezeDiv)rZSymbolicValueErrorrtorchZtensorZint64rZ_get_tensor_rankZ_get_tensor_dim_sizer"ZzerosZ_is_noneZhstackZonesshaperZ JitScalarType from_valueZ onnx_typesqrttyper0)rrrr%r&r'r(r)r*Zframe_step_valueZframe_step_constZframe_length_constsignalZ signal_rankZn_winZwin_length_defaultr r!Zleft_winZ right_winZ torch_windowresultZ sqrt_nfftrrrr Ds$      )NNNFTF)__doc__ functoolstypingrrr6rZ torch.onnxrrrZtorch.onnx._internalrr r __all__partialZ onnx_symbolicZ_onnx_symbolic parse_argsZ GraphContextValueintfloatboolr r"Zbeartyper rrrrsr