import transformers
import diffusers


def load_lumina(_checkpoint_info, diffusers_load_config={}):
    from modules import shared, devices, modelloader, model_quant
    modelloader.hf_login()
    load_config, _quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
    pipe = diffusers.LuminaText2ImgPipeline.from_pretrained(
        'Alpha-VLLM/Lumina-Next-SFT-diffusers',
        cache_dir = shared.opts.diffusers_dir,
        **load_config,
    )
    devices.torch_gc(force=True)
    return pipe


def load_lumina2(checkpoint_info, diffusers_load_config={}):
    from modules import shared, devices, sd_models, model_quant
    repo_id = sd_models.path_to_repo(checkpoint_info.name)

    load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Transformer')
    transformer = diffusers.Lumina2Transformer2DModel.from_pretrained(
        repo_id,
        subfolder="transformer",
        cache_dir=shared.opts.hfcache_dir,
        **load_config,
        **quant_config,
    )

    load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
    text_encoder = transformers.AutoModel.from_pretrained(
        repo_id,
        subfolder="text_encoder",
        cache_dir=shared.opts.hfcache_dir,
        torch_dtype=devices.dtype,
        **load_config,
        **quant_config,
    )

    load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
    pipe = diffusers.Lumina2Text2ImgPipeline.from_pretrained(
        repo_id,
        cache_dir=shared.opts.diffusers_dir,
        text_encoder=text_encoder,
        transformer=transformer,
        **load_config,
    )

    devices.torch_gc(force=True)
    return pipe