import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig

DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32


def preload():
    print("Preloading models to disk...")

    quant_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=DTYPE,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=True,
    )

    print("Preloading GemmaECG-Vision...")
    AutoProcessor.from_pretrained("yasserrmd/GemmaECG-Vision")
    AutoModelForMultimodalLM.from_pretrained(
        "yasserrmd/GemmaECG-Vision",
        device_map="cpu",  # Use CPU for build time to avoid needing GPU in Docker build
        quantization_config=quant_config,
        low_cpu_mem_usage=True,
    )

    print("Preloading Google TabFM...")
    try:
        from tabfm import tabfm_v1_0_0_pytorch as tabfm_v1_0_0
        tabfm_v1_0_0.load(model_type="classification")
    except Exception as e:
        print(f"Warning: Could not preload TabFM model: {e}")

    print("Groq is used for reasoning/report synthesis and does not require local model preload.")
    print("All models preloaded successfully.")


if __name__ == "__main__":
    preload()