"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class UnivNetConfig(PretrainedConfig):
    model_type = "univnet"
    def __init__(self, model_in_channels=64, model_hidden_channels=32, num_mel_bins=100, resblock_kernel_sizes=[3, 3, 3], resblock_stride_sizes=[8, 8, 4],
    resblock_dilation_sizes=[[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]], kernel_predictor_num_blocks=3, kernel_predictor_hidden_channels=64, kernel_predictor_conv_size=3,
    kernel_predictor_dropout=0.0, initializer_range=0.01, leaky_relu_slope=0.2, **kwargs):
        if not (len(resblock_kernel_sizes) == len(resblock_stride_sizes) == len(resblock_dilation_sizes)): raise ValueError("`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).")
        self.model_in_channels = model_in_channels
        self.model_hidden_channels = model_hidden_channels
        self.num_mel_bins = num_mel_bins
        self.resblock_kernel_sizes = resblock_kernel_sizes
        self.resblock_stride_sizes = resblock_stride_sizes
        self.resblock_dilation_sizes = resblock_dilation_sizes
        self.kernel_predictor_num_blocks = kernel_predictor_num_blocks
        self.kernel_predictor_hidden_channels = kernel_predictor_hidden_channels
        self.kernel_predictor_conv_size = kernel_predictor_conv_size
        self.kernel_predictor_dropout = kernel_predictor_dropout
        self.initializer_range = initializer_range
        self.leaky_relu_slope = leaky_relu_slope
        super().__init__(**kwargs)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
