import math
import torch
# This is collected from original pytorch github

class SOTAs(torch.optim.Optimizer):
    r"""Implements Adam algorithm.

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.
    The implementation of the L2 penalty follows changes proposed in
    `Decoupled Weight Decay Regularization`_.

    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
    .. _Decoupled Weight Decay Regularization:
        https://arxiv.org/abs/1711.05101
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(self, model, loss, gammas=(0.9, 0.9), lr=1e-3, betas=(0.9, 0.999), eps=1e-8, update = 'Adam', momentum=0.9, nesterov=False,
                 dampening=0, weight_decay=0, amsgrad=False):
        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]))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
        #TODO!  
        def get_parameters(params):
            for p in params:
                yield p
        self.params = get_parameters(list(model.parameters())) 
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = model
        self.lr = lr
        self.update = update
        self.T = 0
        self.loss = loss
        self.loss.set_gammas(gamma_0=gammas[0], gamma_1=gammas[1])
        defaults = dict(lr=lr, betas=betas, eps=eps, momentum=momentum, nesterov=nesterov, dampening=dampening, 
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(SOTAs, self).__init__(self.params, defaults)

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

    @torch.no_grad()
    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:
            with torch.enable_grad():
                loss = closure()

        for group in self.param_groups:
            if self.update == 'Adam':
              self.lr = group['lr']
              for i, p in enumerate(group['params']):
                  if p.grad is None:
                      continue
                  grad = p.grad
                  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, memory_format=torch.preserve_format)
                      # Exponential moving average of squared gradient values
                      state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                      if amsgrad:
                          # Maintains max of all exp. moving avg. of sq. grad. values
                          state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                  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
                  bias_correction1 = 1 - beta1 ** state['step']
                  bias_correction2 = 1 - beta2 ** state['step']
                  if group['weight_decay'] != 0:
                      grad = grad.add(p, alpha=group['weight_decay'])
                  exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
                  exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                  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() / math.sqrt(bias_correction2)).add_(group['eps'])
                  else:
                      denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                  step_size = group['lr'] / bias_correction1
                  p.addcdiv_(exp_avg, denom, value=-step_size)
            elif self.update == 'SGD':
              weight_decay = group['weight_decay']
              momentum = group['momentum']
              dampening = group['dampening']
              nesterov = group['nesterov']
              self.lr = group['lr']
              for p in group['params']:
                  if p.grad is None:
                      continue
                  d_p = p.grad
                  if weight_decay != 0:
                      d_p = d_p.add(p, alpha=weight_decay) # d_p = (d_p + p*weight_decy)
                  if momentum != 0:
                      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) # [v = v*beta + d_p ] --> new d_p
                      if nesterov:
                          d_p = d_p.add(buf, alpha=momentum)
                      else:
                          d_p = buf
                  p.add_(d_p, alpha=-group['lr'])
        self.T += 1  

        return loss
    def update_stepsize(self, decay_factor=None):
        if decay_factor != None:
            self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor
            print ('Reducing learning rate to %.5f @ T=%s!'%(self.param_groups[0]['lr'], self.T))
            self.loss.update_gammas(decay_factor)
        print ('Updating stepsize @ T=%s!'%(self.T))
        self.T = 0
