"""Hugging Face configuration for Vortex Alpha."""

from __future__ import annotations

from transformers import PretrainedConfig


class VortexConfig(PretrainedConfig):
    model_type = "vortex"

    def __init__(
        self,
        vocab_size: int = 8192,
        hidden_size: int = 1024,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 16,
        num_key_value_heads: int = 4,
        head_dim: int = 64,
        intermediate_size: int = 3664,
        hidden_act: str = "silu",
        max_position_embeddings: int = 4096,
        rope_theta: float = 100000.0,
        rms_norm_eps: float = 1e-5,
        attention_bias: bool = False,
        tie_word_embeddings: bool = True,
        use_cache: bool = False,
        **kwargs,
    ) -> None:
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **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.num_key_value_heads = num_key_value_heads
        self.head_dim = head_dim
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rms_norm_eps = rms_norm_eps
        self.attention_bias = attention_bias
        self.tie_word_embeddings = tie_word_embeddings
        self.use_cache = use_cache