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# http://www.apache.org/licenses/LICENSE-2.0
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import omegaconf

from cosmos_predict1.diffusion.module.pretrained_vae import JITVAE, JointImageVideoSharedJITTokenizer, VideoJITTokenizer
from cosmos_predict1.utils.lazy_config import LazyCall as L

TOKENIZER_OPTIONS = {}


def tokenizer_register(key):
    def decorator(func):
        TOKENIZER_OPTIONS[key] = func
        return func

    return decorator


@tokenizer_register("cosmos_diffusion_tokenizer_comp8x8x8")
def get_cosmos_diffusion_tokenizer_comp8x8x8(resolution: str, chunk_duration: int) -> omegaconf.dictconfig.DictConfig:
    assert resolution in ["720"]

    pixel_chunk_duration = chunk_duration
    temporal_compression_factor = 8
    spatial_compression_factor = 8

    return L(JointImageVideoSharedJITTokenizer)(
        video_vae=L(VideoJITTokenizer)(
            name="cosmos_predict1_tokenizer",
            latent_ch=16,
            is_bf16=True,
            pixel_chunk_duration=pixel_chunk_duration,
            temporal_compression_factor=temporal_compression_factor,
            spatial_compression_factor=spatial_compression_factor,
            spatial_resolution=resolution,
        ),
        image_vae=L(JITVAE)(
            name="cosmos_predict1_tokenizer",
            latent_ch=16,
            is_image=False,
            is_bf16=True,
        ),
        name="cosmos_predict1_tokenizer",
        latent_ch=16,
    )
