from transformers.configuration_utils import PretrainedConfig


class AliceAIConfig(PretrainedConfig):
    model_type = "alice_ai"

    def __init__(
        self,
        vocab_size: int = 129024,
        hidden_size: int = 2048,
        num_hidden_layers: int = 48,
        num_attention_heads: int = 16,
        num_key_value_heads: int = 2,
        head_dim: int = 256,
        linear_num_key_heads: int = 32,
        linear_num_value_heads: int = 32,
        linear_key_head_dim: int = 128,
        linear_value_head_dim: int = 128,
        linear_conv_kernel_dim: int = 4,
        num_experts: int = 512,
        num_experts_per_tok: int = 10,
        moe_intermediate_size: int = 512,
        shared_expert_intermediate_size: int = 512,
        block_attn_res_block_size: int = 4,
        router_score_function: str = "sigmoid",
        router_bias_correction: bool = True,
        kda_allow_negative_eigenvalues: bool = False,
        max_position_embeddings: int = 262144,
        rope_theta: float = 1_000_000.0,
        partial_rotary_factor: float = 0.25,
        rms_norm_eps: float = 1e-6,
        hidden_act: str = "silu",
        initializer_range: float = 0.02,
        attention_dropout: float = 0.0,
        use_cache: bool = True,
        output_router_logits: bool = False,
        layer_types: list[str] | None = None,
        tie_word_embeddings: bool = False,
        pad_token_id: int | None = None,
        bos_token_id: int | None = None,
        eos_token_id: int | list[int] | None = None,
        **kwargs,
    ) -> None:
        if layer_types is None:
            layer_types = [
                "full_attention" if (layer_idx + 1) % 4 == 0 else "linear_attention"
                for layer_idx in range(num_hidden_layers)
            ]
        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,
        )
        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.linear_num_key_heads = linear_num_key_heads
        self.linear_num_value_heads = linear_num_value_heads
        self.linear_key_head_dim = linear_key_head_dim
        self.linear_value_head_dim = linear_value_head_dim
        self.linear_conv_kernel_dim = linear_conv_kernel_dim
        self.num_experts = num_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.moe_intermediate_size = moe_intermediate_size
        self.shared_expert_intermediate_size = shared_expert_intermediate_size
        self.block_attn_res_block_size = block_attn_res_block_size
        self.router_score_function = router_score_function
        self.router_bias_correction = router_bias_correction
        self.kda_allow_negative_eigenvalues = kda_allow_negative_eigenvalues
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.partial_rotary_factor = partial_rotary_factor
        self.rms_norm_eps = rms_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout
        self.use_cache = use_cache
        self.output_router_logits = output_router_logits
        self.layer_types = layer_types
        self.number_of_conv_states = 3
        self._validate_fields()

    def _validate_fields(self) -> None:
        if self.block_attn_res_block_size <= 0:
            raise ValueError("block_attn_res_block_size must be positive")
        if self.linear_conv_kernel_dim < 2:
            raise ValueError("linear_conv_kernel_dim must be at least 2")
        if self.router_score_function != "sigmoid":
            raise ValueError("This architecture requires sigmoid routing")
        if not 0 < self.num_experts_per_tok <= self.num_experts:
            raise ValueError("num_experts_per_tok must be between 1 and num_experts")
        if self.num_attention_heads % self.num_key_value_heads != 0:
            raise ValueError(
                "num_attention_heads must be divisible by num_key_value_heads"
            )
        if self.linear_num_value_heads % self.linear_num_key_heads != 0:
            raise ValueError(
                "linear_num_value_heads must be divisible by linear_num_key_heads"
            )
        if len(self.layer_types) != self.num_hidden_layers:
            raise ValueError("layer_types must contain one entry per hidden layer")
        unknown = set(self.layer_types) - {"linear_attention", "full_attention"}
        if unknown:
            raise ValueError(f"Unsupported layer types: {sorted(unknown)}")