import transformers
import diffusers
from modules import shared, devices, sd_models, shared_items, sd_hijack_te
from modules.logger import log


def load_meissonic(checkpoint_info, diffusers_load_config=None):
    from pipelines.meissonic.transformer import Transformer2DModel as TransformerMeissonic
    from pipelines.meissonic.scheduler import Scheduler as MeissonicScheduler
    from pipelines.meissonic.pipeline import MeissonicPipeline
    from pipelines.meissonic.pipeline_img2img import MeissonicImg2ImgPipeline
    from pipelines.meissonic.pipeline_inpaint import MeissonicInpaintPipeline
    shared_items.pipelines['Meissonic'] = MeissonicPipeline

    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)

    diffusers_load_config['variant'] = 'fp16'
    diffusers_load_config['trust_remote_code'] = True

    log.debug(f'Load model: type=Meissonic repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
    model = TransformerMeissonic.from_pretrained(
        repo_id,
        subfolder="transformer",
        cache_dir=shared.opts.diffusers_dir,
        **diffusers_load_config,
    )
    vqvae = diffusers.VQModel.from_pretrained(
        repo_id,
        subfolder="vqvae",
        cache_dir=shared.opts.diffusers_dir,
        **diffusers_load_config,
    )
    text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained(
        repo_id,
        subfolder="text_encoder",
        cache_dir=shared.opts.diffusers_dir,
    )
    tokenizer = transformers.CLIPTokenizer.from_pretrained(
        repo_id,
        subfolder="tokenizer",
        cache_dir=shared.opts.diffusers_dir,
    )
    scheduler = MeissonicScheduler.from_pretrained(
        repo_id,
        subfolder="scheduler",
        cache_dir=shared.opts.diffusers_dir,
    )
    pipe = MeissonicPipeline(
            vqvae=vqvae.to(devices.dtype),
            text_encoder=text_encoder.to(devices.dtype),
            transformer=model.to(devices.dtype),
            tokenizer=tokenizer,
            scheduler=scheduler,
    )

    diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["meissonic"] = MeissonicPipeline
    diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["meissonic"] = MeissonicImg2ImgPipeline
    diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["meissonic"] = MeissonicInpaintPipeline
    sd_hijack_te.init_hijack(pipe)

    devices.torch_gc(force=True, reason='load')
    return pipe