"""ForgePlex-M2 model configuration for Hugging Face Transformers."""

from transformers import PretrainedConfig


class ForgePlexM2Config(PretrainedConfig):
    model_type = "forgeplex_m2"

    def __init__(
        self,
        vocab_size: int = 4096,
        hidden_size: int = 256,
        num_hidden_layers: int = 11,
        num_attention_heads: int = 8,
        num_key_value_heads: int = 2,
        head_dim: int = 32,
        intermediate_size: int = 707,
        max_position_embeddings: int = 1024,
        rope_theta: float = 5000.0,
        rms_norm_eps: float = 1e-6,
        tie_word_embeddings: bool = True,
        use_xsa_projection: bool = False,
        use_attn_output_gate: bool = True,
        use_refresh_gate: bool = True,
        inject_layers: list | tuple | None = None,
        refresh_kernel: int = 9,
        bos_token_id: int = 0,
        eos_token_id: int = 0,
        pad_token_id: int = 1,
        **kwargs,
    ):
        if inject_layers is None:
            inject_layers = [5, 10]
        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.num_key_value_heads = num_key_value_heads
        self.head_dim = head_dim
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rms_norm_eps = rms_norm_eps
        self.use_xsa_projection = use_xsa_projection
        self.use_attn_output_gate = use_attn_output_gate
        self.use_refresh_gate = use_refresh_gate
        self.inject_layers = list(int(i) for i in inject_layers)
        self.refresh_kernel = refresh_kernel
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            **kwargs,
        )