import torch
from torch.optim.optimizer import Optimizer, required


class SGLD(Optimizer):
    """Implements SGLD algorithm based on
        https://www.ics.uci.edu/~welling/publications/papers/stoclangevin_v6.pdf

    Built on the PyTorch SGD implementation
    (https://github.com/pytorch/pytorch/blob/v1.4.0/torch/optim/sgd.py)
    """
    #X_hat = model.inverse(Z).detach().cpu().numpy()
    def __init__(self,
                 params,
                 lr=required,
                 momentum=0,
                 dampening=0,
                 weight_decay=0,
                 nesterov=False,
                 base_dist=None,
                 G=None,
                 device='cpu',
                 temperature=None
                ):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if momentum < 0.0:
            raise ValueError("Invalid momentum value: {}".format(momentum))
        if weight_decay < 0.0:
            raise ValueError(
                "Invalid weight_decay value: {}".format(weight_decay))

        defaults = dict(lr=lr,
                        momentum=momentum,
                        dampening=dampening,
                        weight_decay=weight_decay,
                        nesterov=nesterov,
                        device=device,
                        temperature=temperature)
        if nesterov and (momentum <= 0 or dampening != 0):
            raise ValueError(
                "Nesterov momentum requires a momentum and zero dampening")
        super(SGLD, self).__init__(params, defaults)

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

    def step(self, closure=None, input_noise=None):
        #if input_noise is not None:
            #print('Not none')
        #if input_noise is None:
            #print("input noise is none")
        #else:
            #print("trace-class noise")
            #print("input noise:{}".format(input_noise))
        """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:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']

            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad.data
                if weight_decay != 0:
                    d_p.add_(p.data, alpha=weight_decay)
                if momentum != 0:
                    #print('momenum:{}'.format(momentum))
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        buf = param_state['momentum_buffer'] = torch.clone(
                            d_p).detach()
                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
                    if nesterov:
                        d_p = d_p.add(momentum, buf)
                    else:
                        d_p = buf

                p.data.add_(d_p, alpha=-group['lr'])
                noise_std = torch.Tensor([2 * group['lr'] * group['temperature']])
                noise_std = noise_std.sqrt().to(group['device'])
                #print("noise_std:{}".format(noise_std))
                if input_noise is not None: 
                    noise = input_noise.to(group['device']) * noise_std
                else:
                    noise = p.data.new(p.data.size()).normal_(mean=0,std=1) * noise_std
                
                #print('group[pos_mask]:{}'.format(group['pos_mask']))
                #p.data[:,~group['pos_mask'],:].add_(noise[:,~group['pos_mask'],:])
                #p.data[:, group['pos_mask'],:].add_(noise[:,group['pos_mask'],:]*0.1)
                p.data.add_(noise)

        return 1.0