"""Configuration class for the MiniQwen3 standalone model.

Mirrors HF's `Qwen3Config` but with every architectural knob exposed
as a constructor kwarg, so this file is the single source of truth for
ablation experiments. Keep `MiniQwen3Config.model_type` unique so the
HF Auto* registry never silently falls back to the upstream Qwen3
implementation.
"""

from __future__ import annotations

from transformers import PretrainedConfig


class MiniQwen3Config(PretrainedConfig):
    """Standalone, easy-to-edit Qwen3-style transformer config.

    Defaults target ~0.5B parameters (24L x d=1024, 16 attn heads,
    GQA 2:1, SwiGLU MLP 3072, RoPE theta=1e6, tied embeddings, QK-Norm).
    """

    model_type = "mini_qwen3"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = None
    base_model_pp_plan = None

    def __init__(
        self,
        vocab_size: int = 151936,
        hidden_size: int = 1024,
        intermediate_size: int = 3072,
        num_hidden_layers: int = 24,
        num_attention_heads: int = 16,
        num_key_value_heads: int = 8,
        head_dim: int = 64,
        hidden_act: str = "silu",
        max_position_embeddings: int = 4096,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-6,
        use_cache: bool = True,
        rope_theta: float = 1_000_000.0,
        attention_bias: bool = False,
        mlp_bias: bool = False,
        attention_dropout: float = 0.0,
        tie_word_embeddings: bool = True,
        use_qk_norm: bool = True,
        pad_token_id: int | None = None,
        bos_token_id: int | None = 151643,
        eos_token_id: int | None = 151643,
        **kwargs,
    ) -> None:
        if num_attention_heads % num_key_value_heads != 0:
            raise ValueError(
                f"num_attention_heads ({num_attention_heads}) must be divisible "
                f"by num_key_value_heads ({num_key_value_heads}) for GQA."
            )

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_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.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.mlp_bias = mlp_bias
        self.attention_dropout = attention_dropout
        self.use_qk_norm = use_qk_norm

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )