"""Configuration for Chest2Vec — a LoRA-tuned Qwen3-Embedding model for
chest radiology report embeddings.

Chest2Vec = Qwen3-Embedding base + contrastive LoRA adapter. It produces a
single L2-normalized report embedding (last-token / EOS pooling), matching the
Qwen3-Embedding convention.
"""
from transformers import PretrainedConfig


class Chest2VecConfig(PretrainedConfig):
    model_type = "chest2vec"

    def __init__(
        self,
        base_model: str = "Qwen/Qwen3-Embedding-0.6B",
        adapter_subdir: str = "contrastive",
        require_flash_attention_2: bool = True,
        default_max_len: int = 512,
        **kwargs,
    ):
        self.base_model = base_model
        self.adapter_subdir = adapter_subdir
        self.require_flash_attention_2 = require_flash_attention_2
        self.default_max_len = default_max_len
        super().__init__(**kwargs)