from transformers.configuration_utils import PretrainedConfig
from typing import Tuple, List, Optional

class NegativeConfig(PretrainedConfig):
    model_type = "negative"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: int = 2564,
        hidden_size: int = 128,
        num_hidden_layers: int = 21,
        num_attention_heads: int = 4,
        num_key_value_heads: int = 2,
        intermediate_size: int = 345,
        swiglu_interval: int = 3,
        num_lanes: int = 4,
        use_engram: bool = True,
        engram_entries: int = 2400,
        engram_ngram_orders: Tuple[int, ...] = (2, 3),
        use_xsa: bool = False,
        use_per_head_gating: bool = False,
        max_position_embeddings: int = 2048,
        rope_theta: float = 2500.0,
        rms_norm_eps: float = 1e-5,
        tie_word_embeddings: bool = True,
        use_cache: bool = False,
        initializer_range: float = 0.02,
        **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.num_key_value_heads = num_key_value_heads
        self.intermediate_size = intermediate_size
        self.swiglu_interval = swiglu_interval
        self.num_lanes = num_lanes
        self.use_engram = use_engram
        self.engram_entries = engram_entries
        self.engram_ngram_orders = list(engram_ngram_orders)
        self.use_xsa = use_xsa
        self.use_per_head_gating = use_per_head_gating
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rms_norm_eps = rms_norm_eps
        self.initializer_range = initializer_range
        self.head_dim = hidden_size // num_attention_heads
        self.auto_map = {
            "AutoConfig": "configuration_negative.NegativeConfig",
            "AutoModel": "modeling_negative.NegativeModel",
            "AutoModelForCausalLM": "modeling_negative.NegativeModelForCausalLM",
        }

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            use_cache=use_cache,
            **kwargs,
        )