from transformers import PretrainedConfig


class VeyraConfig(PretrainedConfig):
    model_type = "veyra"

    def __init__(
        self,
        vocab_size=8192,
        d_model=512,
        hidden_size=512,
        n_q_heads=8,
        num_attention_heads=8,
        n_kv_heads=2,
        num_key_value_heads=2,
        intermediate_size=2048,
        max_seq_len=1024,
        max_position_embeddings=1024,
        layer_pattern=("A", "M", "A", "M", "A", "M", "A", "M"),
        num_hidden_layers=8,
        rms_norm_eps=1e-6,
        rope_theta=10000.0,
        tie_word_embeddings=True,
        bos_token_id=0,
        eos_token_id=1,
        pad_token_id=2,
        unk_token_id=3,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.hidden_size = hidden_size

        self.n_q_heads = n_q_heads
        self.num_attention_heads = num_attention_heads
        self.n_kv_heads = n_kv_heads
        self.num_key_value_heads = num_key_value_heads

        self.intermediate_size = intermediate_size
        self.max_seq_len = max_seq_len
        self.max_position_embeddings = max_position_embeddings

        self.layer_pattern = list(layer_pattern)
        self.num_hidden_layers = num_hidden_layers

        self.rms_norm_eps = rms_norm_eps
        self.rope_theta = rope_theta
        self.tie_word_embeddings = tie_word_embeddings

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            unk_token_id=unk_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )