from transformers import PretrainedConfig


class RNAErnie2Config(PretrainedConfig):
    model_type = "rnaernie2"

    auto_map = {
        "AutoConfig": "configuration_rnaernie2.RNAErnie2Config",
        "AutoModel": "modeling_rnaernie2.RNAErnie2Model",
        "AutoModelForMaskedLM": "modeling_rnaernie2.RNAErnie2ForMaskedLM",
    }

    def __init__(
        self,
        vocab_size: int = 11,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        hidden_act: str = "gelu",
        hidden_dropout_prob: float = 0.1,
        attention_probs_dropout_prob: float = 0.1,
        max_position_embeddings: int = 2048,
        type_vocab_size: int = 2,
        layer_norm_eps: float = 1e-5,
        pad_token_id: int = 0,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **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.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps