from __future__ import division
from __future__ import unicode_literals

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.
    """
    def __init__(self, parameters, decay, use_num_updates=True):
        """
        Args:
          parameters: Iterable of `torch.nn.Parameter`; usually the result of
            `model.parameters()`.
          decay: The exponential decay.
          use_num_updates: Whether to use number of updates when computing
            averages.
        """
        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
        self.shadow_params = [p.clone().detach()
                              for p in parameters if p.requires_grad]

    def update(self, parameters):
        """
        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.
        """
        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):
                s_param.sub_(one_minus_decay * (s_param - param))

    def copy_to(self, parameters):
        """
        Copies current parameters into given collection of parameters.

        Args: 
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored moving averages.
        """
        self.collected_parameters = []
        for s_param, param in zip(self.shadow_params, parameters):
            self.collected_parameters.append(param.clone())
            if param.requires_grad:
                param.data.copy_(s_param.data)

    def store(self, parameters):
        """
        Save the current parameters for restore.

        Args: 
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored moving averages.
        """
        self.collected_parameters = []
        for param in parameters:
            self.collected_parameters.append(param.clone())

    def restore(self, parameters):
        """
        Restore the parameters from the `store` function.
        Usually used in validation. Want to validate the model with EMA parameters without affecting the original optimization process.
        Store the parameters before the `copy_to` function.
        After the validation(or model saving), restore the former parameters.

        Args: 
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored moving averages.
        """
        for c_param, param in zip(self.collected_parameters, parameters):
            if param.requires_grad:
                param.data.copy_(c_param.data)