from transformers import PretrainedConfig

class GPT2CustomConfig(PretrainedConfig):
    model_type = "gpt2_custom"
    auto_map = {
        "AutoConfig": "configuration_gpt2.GPT2CustomConfig",
        "AutoModelForCausalLM": "modeling_gpt2.GPT2CustomLMHeadModel"
    }
    
    def __init__(
        self,
        vocab_size=16384,
        n_positions=512,
        n_embd=512,
        n_layer=12,
        n_head=8,
        n_inner=1360,
        activation_function="swiglu",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        bos_token_id=0,
        eos_token_id=0,
        **kwargs
    ):
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_inner = n_inner
        self.activation_function = activation_function
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range