"""
    #################################################################################################################################################################################################
    # 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 VitsConfig(PretrainedConfig):
    model_type = "vits"
    def __init__(self, vocab_size=38, hidden_size=192, num_hidden_layers=6, num_attention_heads=2, window_size=4, use_bias=True, ffn_dim=768, layerdrop=0.1, ffn_kernel_size=3,
    flow_size=192, spectrogram_bins=513, hidden_act="relu", hidden_dropout=0.1, attention_dropout=0.1, activation_dropout=0.1, initializer_range=0.02, layer_norm_eps=1e-5,
    use_stochastic_duration_prediction=True, num_speakers=1, speaker_embedding_size=0, upsample_initial_channel=512, upsample_rates=[8, 8, 2, 2], upsample_kernel_sizes=[16, 16, 4, 4],
    resblock_kernel_sizes=[3, 7, 11], resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], leaky_relu_slope=0.1, depth_separable_channels=2, depth_separable_num_layers=3,
    duration_predictor_flow_bins=10, duration_predictor_tail_bound=5.0, duration_predictor_kernel_size=3, duration_predictor_dropout=0.5, duration_predictor_num_flows=4,
    duration_predictor_filter_channels=256, prior_encoder_num_flows=4, prior_encoder_num_wavenet_layers=4, posterior_encoder_num_wavenet_layers=16, wavenet_kernel_size=5,
    wavenet_dilation_rate=1, wavenet_dropout=0.0, speaking_rate=1.0, noise_scale=0.667, noise_scale_duration=0.8, sampling_rate=16_000, **kwargs):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.window_size = window_size
        self.use_bias = use_bias
        self.ffn_dim = ffn_dim
        self.layerdrop = layerdrop
        self.ffn_kernel_size = ffn_kernel_size
        self.flow_size = flow_size
        self.spectrogram_bins = spectrogram_bins
        self.hidden_act = hidden_act
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_stochastic_duration_prediction = use_stochastic_duration_prediction
        self.num_speakers = num_speakers
        self.speaker_embedding_size = speaker_embedding_size
        self.upsample_initial_channel = upsample_initial_channel
        self.upsample_rates = upsample_rates
        self.upsample_kernel_sizes = upsample_kernel_sizes
        self.resblock_kernel_sizes = resblock_kernel_sizes
        self.resblock_dilation_sizes = resblock_dilation_sizes
        self.leaky_relu_slope = leaky_relu_slope
        self.depth_separable_channels = depth_separable_channels
        self.depth_separable_num_layers = depth_separable_num_layers
        self.duration_predictor_flow_bins = duration_predictor_flow_bins
        self.duration_predictor_tail_bound = duration_predictor_tail_bound
        self.duration_predictor_kernel_size = duration_predictor_kernel_size
        self.duration_predictor_dropout = duration_predictor_dropout
        self.duration_predictor_num_flows = duration_predictor_num_flows
        self.duration_predictor_filter_channels = duration_predictor_filter_channels
        self.prior_encoder_num_flows = prior_encoder_num_flows
        self.prior_encoder_num_wavenet_layers = prior_encoder_num_wavenet_layers
        self.posterior_encoder_num_wavenet_layers = posterior_encoder_num_wavenet_layers
        self.wavenet_kernel_size = wavenet_kernel_size
        self.wavenet_dilation_rate = wavenet_dilation_rate
        self.wavenet_dropout = wavenet_dropout
        self.speaking_rate = speaking_rate
        self.noise_scale = noise_scale
        self.noise_scale_duration = noise_scale_duration
        self.sampling_rate = sampling_rate
        if len(upsample_kernel_sizes) != len(upsample_rates): raise ValueError(f"The length of `upsample_kernel_sizes` ({len(upsample_kernel_sizes)}) must match the length of `upsample_rates` ({len(upsample_rates)})")
        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.                                                                 #
    #################################################################################################################################################################################################
"""
