ZjxdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) z UnivNetModel model configuration)strict)PreTrainedConfig)auto_docstringzdg845/univnet-dev) checkpointc\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S 'S r \ \\ \S 4-\S 'S r\ \\ \S 4-\S'Sr\ \ -\S'Sr\\S'Sr\\S'Sr\\S'Sr\\-\S'Sr\\S'Sr\\S'SrSrg) UnivNetConfiga model_in_channels (`int`, *optional*, defaults to 64): The number of input channels for the UnivNet residual network. This should correspond to `noise_sequence.shape[1]` and the value used in the [`UnivNetFeatureExtractor`] class. model_hidden_channels (`int`, *optional*, defaults to 32): The number of hidden channels of each residual block in the UnivNet residual network. num_mel_bins (`int`, *optional*, defaults to 100): The number of frequency bins in the conditioning log-mel spectrogram. This should correspond to the value used in the [`UnivNetFeatureExtractor`] class. resblock_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[3, 3, 3]`): A tuple of integers defining the kernel sizes of the 1D convolutional layers in the UnivNet residual network. The length of `resblock_kernel_sizes` defines the number of resnet blocks and should match that of `resblock_stride_sizes` and `resblock_dilation_sizes`. resblock_stride_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[8, 8, 4]`): A tuple of integers defining the stride sizes of the 1D convolutional layers in the UnivNet residual network. The length of `resblock_stride_sizes` should match that of `resblock_kernel_sizes` and `resblock_dilation_sizes`. resblock_dilation_sizes (`tuple[tuple[int]]` or `list[list[int]]`, *optional*, defaults to `[[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]]`): A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the UnivNet residual network. The length of `resblock_dilation_sizes` should match that of `resblock_kernel_sizes` and `resblock_stride_sizes`. The length of each nested list in `resblock_dilation_sizes` defines the number of convolutional layers per resnet block. kernel_predictor_num_blocks (`int`, *optional*, defaults to 3): The number of residual blocks in the kernel predictor network, which calculates the kernel and bias for each location variable convolution layer in the UnivNet residual network. kernel_predictor_hidden_channels (`int`, *optional*, defaults to 64): The number of hidden channels for each residual block in the kernel predictor network. kernel_predictor_conv_size (`int`, *optional*, defaults to 3): The kernel size of each 1D convolutional layer in the kernel predictor network. kernel_predictor_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for each residual block in the kernel predictor network. leaky_relu_slope (`float`, *optional*, defaults to 0.2): The angle of the negative slope used by the leaky ReLU activation. Example: ```python >>> from transformers import UnivNetModel, UnivNetConfig >>> # Initializing a Tortoise TTS style configuration >>> configuration = UnivNetConfig() >>> # Initializing a model (with random weights) from the Tortoise TTS style configuration >>> model = UnivNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` univnet@model_in_channels model_hidden_channelsd num_mel_bins)rrr.resblock_kernel_sizes)rresblock_stride_sizes)r rrresblock_dilation_sizesrkernel_predictor_num_blocks kernel_predictor_hidden_channelskernel_predictor_conv_sizegkernel_predictor_dropoutg{Gz?initializer_rangeg?leaky_relu_slopec[UR5[UR5s=:Xa[UR5:Xd O [ S5eg)zOPart of `@strict`-powered validation. Validates the architecture of the config.z`resblock_kernel_sizes`, `resblock_stride_sizes`, and `resblock_dilation_sizes` must all have the same length (which will be the number of resnet blocks in the model).N)lenrrr ValueError)selfs ڂ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/univnet/configuration_univnet.pyvalidate_architecture#UnivNetConfig.validate_architectureZsM ** +s43M3M/N sRUVZVrVrRs sY  tN)__name__ __module__ __qualname____firstlineno____doc__ model_typer int__annotations__rrrlisttuplerrrrrrfloatrr r&__static_attributes__r)r(r%r r s0dJs!#3#L#9B49uS#X6B9B49uS#X6B,YTE\Y'((,.$c.&'',/eck/#u#!e!r(r N) r.huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r)r(r%r:sK'.3#./J$J0JZ  r(