from pathlib import Path
import json
from typing import Iterable, Any
from contextlib import contextmanager

import torch

from diffusers.training_utils import EMAModel as EMAModel_


def save_args(basepath: Path, args, extra={}):
    info = {"args": vars(args)}
    info["args"].update(extra)
    with open(basepath.joinpath("args.json"), "w") as f:
        json.dump(info, f, indent=4)


class AverageMeter:
    avg: Any

    def __init__(self, name=None):
        self.name = name
        self.reset()

    def reset(self):
        self.sum = self.count = self.avg = 0

    def update(self, val, n=1):
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


class EMAModel(EMAModel_):
    @contextmanager
    def apply_temporary(self, parameters: Iterable[torch.nn.Parameter]):
        parameters = list(parameters)
        original_params = [p.clone() for p in parameters]
        self.copy_to(parameters)

        try:
            yield
        finally:
            for o_param, param in zip(original_params, parameters):
                param.data.copy_(o_param.data)
            del original_params