from pathlib import Path
import json
import math
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:
    def __init__(self, inv_gamma=1.0, power=2 / 3):
        self.inv_gamma = inv_gamma
        self.power = power
        self.reset()

    def reset(self):
        self.step = 0
        self.min = math.inf
        self.max = 0.0
        self.avg = 0.0

    def get_decay(self):
        if self.step <= 0:
            return 1

        return (self.step / self.inv_gamma) ** -self.power

    def update(self, val, n=1):
        for _ in range(n):
            self.step += n
            self.avg += (val - self.avg) * self.get_decay()
            self.min = min(self.min, self.avg)
            self.max = max(self.max, self.avg)


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