from transformers import PretrainedConfig


class BertConfig(PretrainedConfig):

    model_type = "bert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        classifier_dropout=None, 
        affine=False, 
        q=5, 
        r=1, 
        center=False, 
        num_iters=1, 
        return_mean=True, 
        return_std=True, 
        normalize=False, 
        do_pcr=True, 
        do_pfsa=True, 
        alpha=1, 
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_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.hidden_act = hidden_act
        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.type_vocab_size = type_vocab_size
        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.classifier_dropout = classifier_dropout
        self.affine = affine

        self.q = q
        self.r = r 
        self.center = center
        self.num_iters = num_iters
        self.return_mean = return_mean
        self.return_std = return_std
        self.normalize = normalize
        self.do_pcr = do_pcr
        self.do_pfsa = do_pfsa
        self.alpha = alpha