# ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------

'''
Codes adapted from https://github.com/NVlabs/LSGM/blob/main/util/ema.py
'''
import warnings

import torch
from torch.optim import Optimizer


class EMA(Optimizer):
    def __init__(self, opt, ema_decay, memory_efficient=False):
        self.ema_decay = ema_decay
        self.apply_ema = self.ema_decay > 0.
        self.optimizer = opt
        self.state = opt.state
        self.param_groups = opt.param_groups
        self.defaults = {}
        self.memory_efficient = memory_efficient

    def step(self, *args, **kwargs):
        # for group in self.optimizer.param_groups:
            # group.setdefault('amsgrad', False)
            # group.setdefault('maximize', False)
            # group.setdefault('foreach', None)
            # group.setdefault('capturable', False)
            # group.setdefault('differentiable', False)
            # group.setdefault('fused', False)
        retval = self.optimizer.step(*args, **kwargs)

        # stop here if we are not applying EMA
        if not self.apply_ema:
            return retval

        ema, params = {}, {}
        for group in self.optimizer.param_groups:
            for i, p in enumerate(group['params']):
                if p.grad is None:
                    continue
                state = self.optimizer.state[p]

                # State initialization
                if 'ema' not in state:
                    state['ema'] = p.data.clone()
                if p.shape not in params:
                    params[p.shape] = {'idx': 0, 'data': []}
                    ema[p.shape] = []

                params[p.shape]['data'].append(p.data)
                ema[p.shape].append(state['ema'])
            
            # def stack(d, dim=0):
                # return torch.stack([di.cpu() for di in d], dim=dim).cuda()

            for i in params:
                if self.memory_efficient:
                    for j in range(len(params[i]['data'])):
                        ema[i][j].mul_(self.ema_decay).add_(params[i]['data'][j], alpha=1. - self.ema_decay)
                    ema[i] = torch.stack(ema[i], dim=0)
                else:
                    params[i]['data'] = torch.stack(params[i]['data'], dim=0)
                    ema[i] = torch.stack(ema[i], dim=0)
                    ema[i].mul_(self.ema_decay).add_(params[i]['data'], alpha=1. - self.ema_decay)

            for p in group['params']:
                if p.grad is None:
                    continue
                idx = params[p.shape]['idx']
                self.optimizer.state[p]['ema'] = ema[p.shape][idx, :]
                params[p.shape]['idx'] += 1

        return retval

    def load_state_dict(self, state_dict):
        super(EMA, self).load_state_dict(state_dict)
        # load_state_dict loads the data to self.state and self.param_groups. We need to pass this data to
        # the underlying optimizer too.
        self.optimizer.state = self.state
        self.optimizer.param_groups = self.param_groups

    def swap_parameters_with_ema(self, store_params_in_ema):
        """ This function swaps parameters with their ema values. It records original parameters in the ema
        parameters, if store_params_in_ema is true."""

        # stop here if we are not applying EMA
        if not self.apply_ema:
            warnings.warn('swap_parameters_with_ema was called when there is no EMA weights.')
            return

        for group in self.optimizer.param_groups:
            for i, p in enumerate(group['params']):
                if not p.requires_grad:
                    continue
                ema = self.optimizer.state[p]['ema']
                if store_params_in_ema:
                    tmp = p.data.detach()
                    p.data = ema.detach()
                    self.optimizer.state[p]['ema'] = tmp
                else:
                    p.data = ema.detach()