"""
Configuration class for Canopy-R3 Recurrent Mixture-of-Experts (MoE) model.
"""

from transformers import PretrainedConfig


class CanopyConfig(PretrainedConfig):
    model_type = "canopy"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: int = 49152,
        max_seq_len: int = 2048,
        d_model: int = 768,
        num_heads: int = 12,
        num_kv_heads: int = 4,
        head_dim: int = 64,
        num_layers: int = 12,
        prelude_layers: int = 3,
        recurrent_layers: int = 6,
        coda_layers: int = 3,
        recurrent_visits: int = 2,
        loop_residual_scale: float = 0.5,
        dense_intermediate_size: int = 2048,
        moe_num_experts: int = 8,
        moe_top_k: int = 2,
        moe_intermediate_size: int = 1536,
        use_thought_bus: bool = True,
        thought_bus_width: int = 192,
        use_visit_adapter: bool = True,
        visit_adapter_rank: int = 8,
        router_aux_loss_coef: float = 0.01,
        router_z_loss_coef: float = 0.001,
        norm_eps: float = 1e-6,
        rope_theta: float = 10000.0,
        tie_word_embeddings: bool = True,
        initializer_range: float = 0.02,
        quant_mode: str = "none",
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_seq_len = max_seq_len
        self.d_model = d_model
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads
        self.head_dim = head_dim
        self.num_layers = num_layers
        self.prelude_layers = prelude_layers
        self.recurrent_layers = recurrent_layers
        self.coda_layers = coda_layers
        self.recurrent_visits = recurrent_visits
        self.loop_residual_scale = loop_residual_scale
        self.dense_intermediate_size = dense_intermediate_size
        self.moe_num_experts = moe_num_experts
        self.moe_top_k = moe_top_k
        self.moe_intermediate_size = moe_intermediate_size
        self.use_thought_bus = use_thought_bus
        self.thought_bus_width = thought_bus_width
        self.use_visit_adapter = use_visit_adapter
        self.visit_adapter_rank = visit_adapter_rank
        self.router_aux_loss_coef = router_aux_loss_coef
        self.router_z_loss_coef = router_z_loss_coef
        self.norm_eps = norm_eps
        self.rope_theta = rope_theta
        self.tie_word_embeddings = tie_word_embeddings
        self.initializer_range = initializer_range
        self.quant_mode = quant_mode
        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)