from transformers import PretrainedConfig


class BigBrainLanguageConfig(PretrainedConfig):
    model_type = 'big-brain-lm'

    def __init__(
            self,
            vocab_size=50265,
            hidden_size=768,
            num_hidden_layers=12,
            num_attention_heads=12,
            intermediate_size=3072,
            hidden_act='gelu',
            hidden_dropout_probability=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=512,
            initializer_range=0.02,
            layer_norm_eps=1e-6,
            rope_theta=10000,
            sos_token_id=0,
            pad_token_id=1,
            eos_token_id=2,
            unk_token_id=3,
            **kwargs
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_probability = hidden_dropout_probability
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.rope_theta = rope_theta
        self.sos_token_id = sos_token_id
        self.pad_token_id = pad_token_id
        self.eos_token_id = eos_token_id
        self.unk_token_id = unk_token_id
        super().__init__(**kwargs)