# Modified by Microsoft Corporation.
# Licensed under the MIT license.

# Custom PyTorch optimizer classes, to be registered in net_util.py
import math

import torch


class GlobalAdam(torch.optim.Adam):
    '''
    Global Adam algorithm with shared states for Hogwild.
    Adapted from https://github.com/ikostrikov/pytorch-a3c/blob/master/my_optim.py (MIT)
    '''

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        super().__init__(params, lr, betas, eps, weight_decay)

        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'] = torch.zeros(1)
                state['exp_avg'] = p.data.new().resize_as_(p.data).zero_()
                state['exp_avg_sq'] = p.data.new().resize_as_(p.data).zero_()

    def share_memory(self):
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'].share_memory_()
                state['exp_avg'].share_memory_()
                state['exp_avg_sq'].share_memory_()

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                state = self.state[p]
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']
                state['step'] += 1
                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group['eps'])
                bias_correction1 = 1 - beta1 ** state['step'].item()
                bias_correction2 = 1 - beta2 ** state['step'].item()
                step_size = group['lr'] * math.sqrt(
                    bias_correction2) / bias_correction1
                p.data.addcdiv_(-step_size, exp_avg, denom)
        return loss


class GlobalRMSprop(torch.optim.RMSprop):
    '''
    Global RMSprop algorithm with shared states for Hogwild.
    Adapted from https://github.com/jingweiz/pytorch-rl/blob/master/optims/sharedRMSprop.py (MIT)
    '''

    def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0):
        super().__init__(params, lr=lr, alpha=alpha, eps=eps, weight_decay=weight_decay, momentum=0, centered=False)

        # State initialisation (must be done before step, else will not be shared between threads)
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'] = p.data.new().resize_(1).zero_()
                state['square_avg'] = p.data.new().resize_as_(p.data).zero_()

    def share_memory(self):
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'].share_memory_()
                state['square_avg'].share_memory_()

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                state = self.state[p]
                square_avg = state['square_avg']
                alpha = group['alpha']
                state['step'] += 1
                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
                avg = square_avg.sqrt().add_(group['eps'])
                p.data.addcdiv_(-group['lr'], grad, avg)
        return loss
