from transformers import PretrainedConfig

class HLMEncoderConfig(PretrainedConfig):
    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_dropout_prob=0.1,
        layer_norm_eps=1e-7,
        sandwich=False,
        sandwich_size=0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        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.dropout_prob = hidden_dropout_prob
        self.layer_norm_eps = layer_norm_eps
        if sandwich:
            self.sandwich_size = num_hidden_layers // 6
        else:
            self.sandwich_size = sandwich_size


class HLMConfig(PretrainedConfig):
    model_type = "hlm"

    def __init__(
        self,
        vocab_size=512,
        type_vocab_size=2,
        embedding_size=-1,
        max_seq_length=256,
        max_word_length=16,
        initializer_range=0.02,
        pad_token_id=0,
        intra_word_encoder={},
        inter_word_encoder={},
        residual_word_embedding=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.vocab_size = vocab_size
        self.type_vocab_size = type_vocab_size
        self.embedding_size = embedding_size
        self.initializer_range = initializer_range
        self.max_seq_length = max_seq_length
        self.max_word_length = max_word_length
        self.pad_token_id = pad_token_id
        self.intra_word_encoder = HLMEncoderConfig(**intra_word_encoder)
        self.inter_word_encoder = HLMEncoderConfig(**inter_word_encoder)
        self.hidden_size = self.inter_word_encoder.hidden_size
        self.residual_word_embedding = residual_word_embedding