from transformers.configuration_utils import PretrainedConfig


class TinyConfig(PretrainedConfig):
    """
    Configuration class for TinyStories Transformer.
    """

    model_type = "tiny"

    def __init__(
        self,
        vocab_size=8000,
        hidden_size=384,
        intermediate_size=1024,
        num_hidden_layers=8,
        num_attention_heads=6,
        max_position_embeddings=512,
        rms_norm_eps=1e-5,
        rope_theta=10000.0,
        tie_word_embeddings=True,
        bos_token_id=2,
        eos_token_id=3,
        pad_token_id=0,
        initializer_range=0.02,
        hidden_dropout=0.0,
        attention_dropout=0.0,
        **kwargs,
    ):
        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **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.max_position_embeddings = max_position_embeddings

        self.rms_norm_eps = rms_norm_eps

        self.rope_theta = rope_theta

        self.initializer_range = initializer_range

        self.hidden_dropout = hidden_dropout

        self.attention_dropout = attention_dropout

    @property
    def head_dim(self):
        return self.hidden_size // self.num_attention_heads