from collections import OrderedDict
import copy
import torch
import torch.nn as nn


class ExponentialMovingAverage:
    """
    Maintains moving averages of parameters with exponential decay

    At each step, the stored copy `copy` of each parameter `param` is
    updated as follows:

        `copy = decay * copy + (1 - decay) * param`

    where `decay` is an attribute of the ExponentialMovingAverage object.
    """

    def __init__(self, model: nn.Module, decay: float):
        """
        Args:
            model:
                A torch.nn.Module whose parameters are to be tracked
            decay:
                A value (usually close to 1.) by which updates are
                weighted as part of the above formula
        """
        super(ExponentialMovingAverage, self).__init__()

        self.params = copy.deepcopy(model.state_dict())
        self.decay = decay

    def _update_state_dict_(self, update, state_dict):
        with torch.no_grad():
            for k, v in update.items():
                stored = state_dict[k]
                if not isinstance(v, torch.Tensor):
                    self._update_state_dict_(v, stored)
                else:
                    diff = stored - v
                    diff *= 1 - self.decay
                    stored -= diff

    def update(self, model: torch.nn.Module) -> None:
        """
        Updates the stored parameters using the state dict of the provided
        module. The module should have the same structure as that used to
        initialize the ExponentialMovingAverage object.
        """
        self._update_state_dict_(model.state_dict(), self.params)

    def load_state_dict(self, state_dict: OrderedDict) -> None:
        self.params = state_dict["params"]
        self.decay = state_dict["decay"]

    def state_dict(self) -> OrderedDict:
        return OrderedDict(
            {
                "params": self.params,
                "decay": self.decay,
            }
        )
