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"""
Update the CDFs parameters of a trained model.

To be called on a model checkpoint after training. This will update the internal
CDFs related buffers required for entropy coding.
"""
import argparse
import hashlib
import sys

from pathlib import Path
from typing import Dict

import torch

from compressai.models.priors import (
    FactorizedPrior,
    JointAutoregressiveHierarchicalPriors,
    MeanScaleHyperprior,
    ScaleHyperprior,
)
from compressai.zoo import load_state_dict
from compressai.zoo.image import model_architectures as zoo_models
from Cheng2020Attention import Cheng2020Attention

def sha256_file(filepath: Path, len_hash_prefix: int = 8) -> str:
    # from pytorch github repo
    sha256 = hashlib.sha256()
    with filepath.open("rb") as f:
        while True:
            buf = f.read(8192)
            if len(buf) == 0:
                break
            sha256.update(buf)
    digest = sha256.hexdigest()

    return digest[:len_hash_prefix]


def load_checkpoint(filepath: Path) -> Dict[str, torch.Tensor]:
    checkpoint = torch.load(filepath, map_location="cpu")

    if "network" in checkpoint:
        state_dict = checkpoint["network"]
    elif "state_dict" in checkpoint:
        state_dict = checkpoint["state_dict"]
    else:
        state_dict = checkpoint

    state_dict = load_state_dict(state_dict)
    return state_dict


description = """
Export a trained model to a new checkpoint with an updated CDFs parameters and a
hash prefix, so that it can be loaded later via `load_state_dict_from_url`.
""".strip()

models = {
    "factorized-prior": FactorizedPrior,
    "jarhp": JointAutoregressiveHierarchicalPriors,
    "mean-scale-hyperprior": MeanScaleHyperprior,
    "scale-hyperprior": ScaleHyperprior,
}
models.update(zoo_models)


def setup_args():
    parser = argparse.ArgumentParser(description=description)
    parser.add_argument(
        "filepath", type=str, help="Path to the checkpoint model to be exported."
    )
    parser.add_argument("-n", "--name", type=str, help="Exported model name.")
    parser.add_argument("-d", "--dir", type=str, help="Exported model directory.")
    parser.add_argument(
        "--no-update",
        action="store_true",
        default=False,
        help="Do not update the model CDFs parameters.",
    )
    return parser


def main(argv):
    args = setup_args().parse_args(argv)

    filepath = Path(args.filepath).resolve()
    if not filepath.is_file():
        raise RuntimeError(f'"{filepath}" is not a valid file.')

    state_dict = load_checkpoint(filepath)

    model_cls_or_entrypoint = Cheng2020Attention()

    model_cls = model_cls_or_entrypoint
    net = model_cls.from_state_dict(state_dict)

    if not args.no_update:
        net.update(force=True)
    state_dict = net.state_dict()

    if not args.name:
        filename = filepath
        while filename.suffixes:
            filename = Path(filename.stem)
    else:
        filename = args.name

    ext = "".join(filepath.suffixes)

    if args.dir is not None:
        output_dir = Path(args.dir)
        Path(output_dir).mkdir(exist_ok=True)
    else:
        output_dir = Path.cwd()

    filepath = output_dir / f"{filename}{ext}"
    torch.save(state_dict, filepath)
    hash_prefix = sha256_file(filepath)

    filepath.rename(f"{output_dir}/{filename}-{hash_prefix}{ext}")


if __name__ == "__main__":
    main(sys.argv[1:])