"""
Configuration class for Ivme-Conversate-S-v2-Instruct.

Mirrors train_conversate_s_v2_instruct.ModelConfig exactly (same field
names, same defaults), so config.json produced from a real training run's
ModelConfig round-trips into this class with no field remapping needed.
"""

from transformers import PretrainedConfig


class IvmeConversateSV2InstructConfig(PretrainedConfig):
    model_type = "ivme_conversate_s_v2_instruct"

    def __init__(
        self,
        vocab_size: int = 8000,
        d_model: int = 224,
        n_layers: int = 9,
        n_heads: int = 7,
        d_ff: int = 896,
        max_seq_len: int = 1024,
        norm_eps: float = 1e-5,
        rope_theta: float = 10000.0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.d_ff = d_ff
        self.max_seq_len = max_seq_len
        self.norm_eps = norm_eps
        self.rope_theta = rope_theta
        kwargs.setdefault("tie_word_embeddings", True)
        super().__init__(**kwargs)