from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)


class ProPrimeConfig(PretrainedConfig):
    model_type = "proprime"

    def __init__(
        self,
        vocab_size=33,
        mask_token_id=32,
        pad_token_id=1,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1026,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        position_embedding_type="rotary",
        use_cache=True,
        emb_layer_norm_before=None,
        token_dropout=False,
        flash_attention=True,
        structure_vocab_size=100,
        value_loss_scale=0.01,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id, mask_token_id=mask_token_id, **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_dropout_prob = hidden_dropout_prob
        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.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.emb_layer_norm_before = emb_layer_norm_before
        self.token_dropout = token_dropout
        self.flash_attention = flash_attention
        self.structure_vocab_size = structure_vocab_size
        
ProPrimeConfig.register_for_auto_class()