from transformers.configuration_utils import PretrainedConfig
class FM4BioConfig(PretrainedConfig):
    model_type = "fm4bio"

    def __init__(
        self,
        vocab_size=128,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        hidden_act="swiglu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        max_position_embeddings=2048,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-05,
        pad_token_id=0,
        add_linear_bias=True,
        position_embedding_type="rope",
        normalization_type="RMSNorm",
        use_cache=True,
        rotary_percent=1.0,
        seq_len_interpolation_factor=None,
        moe=False,
        num_experts=0,
        experts_per_token=0,
        use_lm_head=True,
        tie_word_embeddings=True,
        output_vocab_size: int = None,  # when set, the output vocab size is different from the input vocab size
        gradient_checkpointing=False, # << Pan: Gradient checkpoint for memory saving
        **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.add_linear_bias = add_linear_bias
        assert normalization_type in [
            "RMSNorm",
            "LayerNorm",
        ], "normalization_type must be 'RMSNorm' or 'LayerNorm'"
        self.normalization_type = normalization_type
        self.rotary_percent = rotary_percent
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
        self.moe = moe
        self.num_experts = num_experts
        self.experts_per_token = experts_per_token
        self.use_lm_head = use_lm_head
        self.tie_word_embeddings = tie_word_embeddings
        self.output_vocab_size = output_vocab_size
        self.gradient_checkpointing = gradient_checkpointing # << Pan: Gradient checkpoint for memory saving