from transformers.configuration_utils import PretrainedConfig


class SvectorConfig(PretrainedConfig):

    model_type = 'svector'

    def __init__(
        self, 
        n_mels=40, 
        sample_rate=16000, 
        win_length=25, 
        hop_length=10, 
        mean_norm=True, 
        std_norm=False, 
        norm_type='sentence', 
        num_heads=8, 
        num_layers=5, 
        hidden_size=512, 
        num_classes=1251, 
        loss_fn='aam', 
        auto_map={
            "AutoConfig": "configuration_svector.SvectorConfig", 
            "AutoModel": "modeling_svector.SvectorModel", 
            "AutoModelForAudioClassification": "modeling_svector.SvectorModelForSequenceClassification"
        },
        initializer_range=0.02,
        **kwargs
    ):
        # Compute features
        self.n_mels = n_mels
        self.sample_rate = sample_rate
        self.win_length = win_length
        self.hop_length = hop_length

        # Mean variance norm
        self.mean_norm = mean_norm
        self.std_norm = std_norm
        self.norm_type = norm_type

        # Embedding model
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.num_layers = num_layers

        # Classifier
        self.num_classes = num_classes
        self.loss_fn = loss_fn

        # Others
        self.auto_map = auto_map
        self.initializer_range = initializer_range

        super().__init__(**kwargs)