from transformers import PretrainedConfig

class PersadianNanoV4Config(PretrainedConfig):
    model_type = "persadian_nano_v4"
    
    def __init__(
        self,
        hidden_size=512,
        intermediate_size=1024,
        num_hidden_layers=12,
        num_attention_heads=8,
        num_key_value_heads=4,
        num_experts=4,
        num_experts_per_tok=2,
        progressive_experts=True,
        min_experts=1,
        max_experts=2,
        use_hyper_connection=True,
        use_compressed_attention=True,
        compress_ratio=4,
        vocab_size=50257,
        max_position_embeddings=2048,
        bos_token_id=50256,
        eos_token_id=50256,
        rope_theta=10000.0,
        layer_norm_eps=1e-5,
        dropout=0.1,
        attention_dropout=0.0,
        use_flash_attention=True,
        **kwargs
    ):
        super().__init__(**kwargs)
        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.num_experts = num_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.progressive_experts = progressive_experts
        self.min_experts = min_experts
        self.max_experts = max_experts
        self.use_hyper_connection = use_hyper_connection
        self.use_compressed_attention = use_compressed_attention
        self.compress_ratio = compress_ratio
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.rope_theta = rope_theta
        self.layer_norm_eps = layer_norm_eps
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.use_flash_attention = use_flash_attention