"""HuggingFace configuration for PlasmidLM."""

from transformers import PretrainedConfig


class PlasmidLMConfig(PretrainedConfig):
    model_type = "plasmid_lm"

    def __init__(
        self,
        vocab_size: int = 112,
        hidden_size: int = 384,
        num_hidden_layers: int = 10,
        num_attention_heads: int = 8,
        intermediate_size: int = 1536,
        hidden_act: str = "gelu",
        rms_norm_eps: float = 1e-5,
        max_position_embeddings: int = 16384,
        rope_theta: float = 10000.0,
        tie_word_embeddings: bool = True,
        # MoE
        use_moe: bool = False,
        num_experts: int = 6,
        num_experts_per_tok: int = 2,
        moe_intermediate_size: int | None = None,
        aux_loss_coef: float = 0.01,
        # Tokenizer metadata (informational, saved in checkpoint)
        tokenizer_type: str = "char",
        kmer_k: int | None = None,
        kmer_stride: int | None = None,
        **kwargs,
    ):
        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.rms_norm_eps = rms_norm_eps
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        # MoE
        self.use_moe = use_moe
        self.num_experts = num_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.moe_intermediate_size = moe_intermediate_size or intermediate_size
        self.aux_loss_coef = aux_loss_coef
        # Tokenizer metadata
        self.tokenizer_type = tokenizer_type
        self.kmer_k = kmer_k
        self.kmer_stride = kmer_stride
        super().__init__(
            vocab_size=vocab_size,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )