import diffusers
from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
from modules.logger import log
from pipelines import generic


def load_lens(checkpoint_info, diffusers_load_config=None):
    if diffusers_load_config is None:
        diffusers_load_config = {}

    repo_id = sd_models.path_to_repo(checkpoint_info)
    sd_models.hf_auth_check(checkpoint_info)
    from pipelines import lens

    from modules.attention import hijack_kernels
    hijack_kernels()

    load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
    log.debug(f'Load model: type=Lens repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} reasoner={shared.opts.model_lens_enable_pe} args={load_args}')

    transformer = generic.load_transformer(repo_id, cls_name=lens.LensTransformer2DModel, load_config=diffusers_load_config, native_spec=lens.LENS_SPEC)
    text_encoder = generic.load_text_encoder(repo_id, cls_name=lens.LensGptOssEncoder, load_config=diffusers_load_config, allow_quant=False)

    diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["lens"] = lens.LensPipeline
    diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["lens"] = lens.LensImg2ImgPipeline
    diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["lens"] = lens.LensInpaintPipeline
    generic.set_pipeline('Lens', lens.LensPipeline)
    if repo_id is None or repo_id.lower() == 'none':
        return None
    pipe = lens.LensPipeline.from_pretrained(
        repo_id,
        transformer=transformer,
        text_encoder=text_encoder,
        cache_dir=shared.opts.diffusers_dir,
        **load_args,
    )
    pipe.task_args = {
        "output_type": "np",
        "enable_reasoner": shared.opts.model_lens_enable_pe,
    }

    sd_hijack_te.init_hijack(pipe)
    sd_hijack_vae.init_hijack(pipe)
    devices.torch_gc(force=True, reason="load")
    return pipe