from __future__ import division
from __future__ import unicode_literals

from typing import Iterable, Optional
import weakref

import torch


# Partially based on:
# https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
class ExponentialMovingAverage:
    """
    Maintains (exponential) moving average of a set of parameters.

    Args:
        parameters: Iterable of `torch.nn.Parameter` (typically from
            `model.parameters()`).
        decay: The exponential decay.
        use_num_updates: Whether to use number of updates when computing
            averages.
    """
    def __init__(
        self,
        parameters: Iterable[torch.nn.Parameter],
        decay: float,
        use_num_updates: bool = True
    ):
        if decay < 0.0 or decay > 1.0:
            raise ValueError('Decay must be between 0 and 1')
        self.decay = decay
        self.num_updates = 0 if use_num_updates else None
        parameters = list(parameters)
        self.shadow_params = [p.clone().detach()
                              for p in parameters if p.requires_grad]
        self.collected_params = []
        # By maintaining only a weakref to each parameter,
        # we maintain the old GC behaviour of ExponentialMovingAverage:
        # if the model goes out of scope but the ExponentialMovingAverage
        # is kept, no references to the model or its parameters will be
        # maintained, and the model will be cleaned up.
        self._params_refs = [weakref.ref(p) for p in parameters]

    def _get_parameters(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]]
    ) -> Iterable[torch.nn.Parameter]:
        if parameters is None:
            parameters = [p() for p in self._params_refs]
            if any(p is None for p in parameters):
                raise ValueError(
                    "(One of) the parameters with which this "
                    "ExponentialMovingAverage "
                    "was initialized no longer exists (was garbage collected);"
                    " please either provide `parameters` explicitly or keep "
                    "the model to which they belong from being garbage "
                    "collected."
                )
            return parameters
        else:
            return parameters

    def update(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Update currently maintained parameters.

        Call this every time the parameters are updated, such as the result of
        the `optimizer.step()` call.

        Args:
          parameters: Iterable of `torch.nn.Parameter`; usually the same set of
            parameters used to initialize this object. If `None`, the
            parameters with which this `ExponentialMovingAverage` was
            initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        decay = self.decay
        if self.num_updates is not None:
            self.num_updates += 1
            decay = min(
                decay,
                (1 + self.num_updates) / (10 + self.num_updates)
            )
        one_minus_decay = 1.0 - decay
        with torch.no_grad():
            parameters = [p for p in parameters if p.requires_grad]
            for s_param, param in zip(self.shadow_params, parameters):
                tmp = (s_param - param)
                # tmp will be a new tensor so we can do in-place
                tmp.mul_(one_minus_decay)
                s_param.sub_(tmp)

    def copy_to(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Copy current parameters into given collection of parameters.

        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored moving averages. If `None`, the
            parameters with which this `ExponentialMovingAverage` was
            initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        for s_param, param in zip(self.shadow_params, parameters):
            if param.requires_grad:
                param.data.copy_(s_param.data)

    def store(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Save the current parameters for restoring later.

        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            temporarily stored. If `None`, the parameters of with which this
            `ExponentialMovingAverage` was initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        self.collected_params = [param.clone()
                                 for param in parameters
                                 if param.requires_grad]


    def restore(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Restore the parameters stored with the `store` method.
        Useful to validate the model with EMA parameters without affecting the
        original optimization process. Store the parameters before the
        `copy_to` method. After validation (or model saving), use this to
        restore the former parameters.

        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored parameters. If `None`, the
            parameters with which this `ExponentialMovingAverage` was
            initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        for c_param, param in zip(self.collected_params, parameters):
            if param.requires_grad:
                param.data.copy_(c_param.data)