import math
import torch
from torch.optim.optimizer import Optimizer


class LSAdam(Optimizer):
    r"""Implements Adam algorithm.

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False)

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, amsgrad=False, sigma=1.0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(LSAdam, self).__init__(params, defaults)
        
        # LS initialization
        sizes = []
        for param in self.param_groups[0]['params']:
            sizes.append(torch.numel(param))
        
        coeffs = []
        zero_Ns = []
        for size in sizes:
            if size >= 2:
                c = torch.zeros(1, size).cuda()
                c[0, 0] = -2.
                c[0, 1] = 1.
                c[0, -1] = 1.
                zero_N = torch.zeros(1, size).cuda()
                c_fft = torch.rfft(c, 1, onesided=False)
                coeff = 1./(1. - sigma*c_fft[...,0])
                coeffs.append(coeff)
                zero_Ns.append(zero_N)
            else:
                coeffs.append(None)
                zero_Ns.append(None)
        
        self.sigma = sigma
        self.sizes = sizes
        self.coeffs = coeffs
        self.zero_Ns = zero_Ns

    def __setstate__(self, state):
        super(LSAdam, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            idx = 0
            for p in group['params']:
                if p.grad is None:
                    continue
                
                if self.sizes[idx] > 2:
                    # Perform Laplacian smoothing on the stochastic gradient
                    tmp = p.grad.view(-1, self.sizes[idx])
                    tmp1 = tmp.data
                    ft_tmp1 = torch.rfft(tmp1, 1, onesided=False)
                    tmp1 = torch.zeros_like(ft_tmp1)
                    tmp1[...,0] = ft_tmp1[...,0]*self.coeffs[idx]
                    tmp1[...,1] = ft_tmp1[...,1]*self.coeffs[idx]
                    tmp1 = torch.irfft(tmp1, 1, onesided = False)
                    p.grad.data = tmp1.view(p.grad.size())
                    for i in range(100):
                        print('Performed Laplacian smoothing: sigma %f'%self.sigma)
                    
                
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                amsgrad = group['amsgrad']

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsgrad:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    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)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                else:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)
                idx += 1
        return loss

import math
import torch
from torch.optim.optimizer import Optimizer


class LSAdam(Optimizer):
    r"""Implements Adam algorithm.

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False)

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, amsgrad=False, sigma=1.0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(LSAdam, self).__init__(params, defaults)
        
        # LS initialization
        sizes = []
        for param in self.param_groups[0]['params']:
            sizes.append(torch.numel(param))
        
        coeffs = []
        zero_Ns = []
        for size in sizes:
            if size >= 2:
                c = torch.zeros(1, size).cuda()
                c[0, 0] = -2.
                c[0, 1] = 1.
                c[0, -1] = 1.
                zero_N = torch.zeros(1, size).cuda()
                c_fft = torch.rfft(c, 1, onesided=False)
                coeff = 1./(1. - sigma*c_fft[...,0])
                coeffs.append(coeff)
                zero_Ns.append(zero_N)
            else:
                coeffs.append(None)
                zero_Ns.append(None)
        
        self.sigma = sigma
        self.sizes = sizes
        self.coeffs = coeffs
        self.zero_Ns = zero_Ns

    def __setstate__(self, state):
        super(LSAdam, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            idx = 0
            for p in group['params']:
                if p.grad is None:
                    continue
                
                if self.sizes[idx] > 2:
                    # Perform Laplacian smoothing on the stochastic gradient
                    tmp = p.grad.view(-1, self.sizes[idx])
                    tmp1 = tmp.data
                    ft_tmp1 = torch.rfft(tmp1, 1, onesided=False)
                    tmp1 = torch.zeros_like(ft_tmp1)
                    tmp1[...,0] = ft_tmp1[...,0]*self.coeffs[idx]
                    tmp1[...,1] = ft_tmp1[...,1]*self.coeffs[idx]
                    tmp1 = torch.irfft(tmp1, 1, onesided = False)
                    p.grad.data = tmp1.view(p.grad.size())
                    for i in range(100):
                        print('Performed Laplacian smoothing: sigma %f'%self.sigma)
                    
                
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                amsgrad = group['amsgrad']

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsgrad:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    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)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                else:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)
                idx += 1
        return loss

Implements Adam algorithm.      It has been proposed in `Adam: A Method for Stochastic Optimization`_.      Arguments:         params (iterable): iterable of parameters to optimize or dicts defining             parameter groups         lr (float, optional): learning rate (default: 1e-3)         betas (Tuple[float, float], optional): coefficients used for computing             running averages of gradient and its square (default: (0.9, 0.999))         eps (float, optional): term added to the denominator to improve             numerical stability (default: 1e-8)         weight_decay (float, optional): weight decay (L2 penalty) (default: 0)         amsgrad (boolean, optional): whether to use the AMSGrad variant of this             algorithm from the paper `On the Convergence of Adam and Beyond`_             (default: False)      .. _Adam\: A Method for Stochastic Optimization:         https://arxiv.org/abs/1412.6980     .. _On the Convergence of Adam and Beyond:         https://openreview.net/forum?id=ryQu7f-RZ # LS initialization Performs a single optimization step.          Arguments:             closure (callable, optional): A closure that reevaluates the model                 and returns the loss. # Perform Laplacian smoothing on the stochastic gradient # State initialization # Exponential moving average of gradient values # Exponential moving average of squared gradient values # Maintains max of all exp. moving avg. of sq. grad. values # Decay the first and second moment running average coefficient # Maintains the maximum of all 2nd moment running avg. till now # Use the max. for normalizing running avg. of gradient