from transformers import PretrainedConfig

class PebbleConfig(PretrainedConfig):
    model_type = "pebble_10m"

    def __init__(
        self,
        vocab_size=2048,
        hidden_size=384,
        intermediate_size=1536,
        num_hidden_layers=8,
        num_attention_heads=6,
        block_pattern="mmma|mmma",
        hybrid_ratio="3:1 mamba2:attention",
        max_position_embeddings=512,
        rms_norm_eps=1e-6,
        tie_word_embeddings=True,
        mamba2=None,
        attention=None,
        **kwargs,
    ):
        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.block_pattern = block_pattern
        self.hybrid_ratio = hybrid_ratio
        self.max_position_embeddings = max_position_embeddings
        self.rms_norm_eps = rms_norm_eps
        self.tie_word_embeddings = tie_word_embeddings
        
        # Default dictionaries if not provided in config.json
        self.mamba2 = mamba2 or {
            "d_state": 128, "d_conv": 4, "expand": 2, 
            "headdim": 96, "use_mem_eff_path": True
        }
        self.attention = attention or {
            "rope_theta": 10000.0, "is_causal": True
        }
        super().__init__(**kwargs)