import torch
from torch.optim.optimizer import Optimizer

from .types import Betas2, OptFloat, OptLossClosure, Params, State

__all__ = ('SWATS',)


class SWATS(Optimizer):
    r"""Implements SWATS Optimizer Algorithm.
    It has been proposed in `Improving Generalization Performance by
    Switching from Adam to SGD`__.

    Arguments:
        params: iterable of parameters to optimize or dicts defining
            parameter groups
        lr: learning rate (default: 1e-2)
        betas: coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps: term added to the denominator to improve
            numerical stability (default: 1e-3)
        weight_decay: weight decay (L2 penalty) (default: 0)
        amsgrad: whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`
            (default: False)
        nesterov: enables Nesterov momentum (default: False)


    Example:
        >>> import torch_optimizer as optim
        >>> optimizer = optim.SWATS(model.parameters(), lr=0.01)
        >>> optimizer.zero_grad()
        >>> loss_fn(model(input), target).backward()
        >>> optimizer.step()

    __ https://arxiv.org/pdf/1712.07628.pdf

    Note:
        Reference code: https://github.com/Mrpatekful/swats
    """

    def __init__(
        self,
        params: Params,
        lr: float = 1e-3,
        betas: Betas2 = (0.9, 0.999),
        eps: float = 1e-3,
        weight_decay: float = 0,
        amsgrad: bool = False,
        nesterov: bool = 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 weight_decay < 0:
            raise ValueError(
                'Invalid weight_decay value: {}'.format(weight_decay)
            )
        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            phase='ADAM',
            weight_decay=weight_decay,
            amsgrad=amsgrad,
            nesterov=nesterov,
        )

        super().__init__(params, defaults)

    def __setstate__(self, state: State) -> None:
        super().__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)
            group.setdefault('nesterov', False)

    def step(self, closure: OptLossClosure = None) -> OptFloat:
        r"""Performs a single optimization step.

        Arguments:
            closure: 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:
            for w in group['params']:
                if w.grad is None:
                    continue
                grad = w.grad.data

                if grad.is_sparse:
                    raise RuntimeError(
                        'Adam does not support sparse gradients, '
                        'please consider SparseAdam instead'
                    )

                amsgrad = group['amsgrad']

                state = self.state[w]

                # state initialization
                if len(state) == 0:
                    state['step'] = 0
                    # exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(w.data)
                    # exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(w.data)
                    # moving average for the non-orthogonal projection scaling
                    state['exp_avg2'] = w.new(1).fill_(0)
                    if amsgrad:
                        # maintains max of all exp. moving avg.
                        # of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(w.data)

                exp_avg, exp_avg2, exp_avg_sq = (
                    state['exp_avg'],
                    state['exp_avg2'],
                    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_(w.data, alpha=group['weight_decay'])

                # if its SGD phase, take an SGD update and continue
                if group['phase'] == 'SGD':
                    if 'momentum_buffer' not in state:
                        buf = state['momentum_buffer'] = torch.clone(
                            grad
                        ).detach()
                    else:
                        buf = state['momentum_buffer']
                        buf.mul_(beta1).add_(grad)
                        grad = buf

                    grad.mul_(1 - beta1)
                    if group['nesterov']:
                        grad.add_(buf, alpha=beta1)

                    w.data.add_(grad, alpha=-group['lr'])
                    continue

                # decay the first and second moment running average coefficient
                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().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'] * (bias_correction2 ** 0.5) / bias_correction1
                )

                p = -step_size * (exp_avg / denom)
                w.data.add_(p)

                p_view = p.view(-1)
                pg = p_view.dot(grad.view(-1))

                if pg != 0:
                    # the non-orthognal scaling estimate
                    scaling = p_view.dot(p_view) / -pg
                    exp_avg2.mul_(beta2).add_(scaling, alpha=1 - beta2)

                    # bias corrected exponential average
                    corrected_exp_avg = exp_avg2 / bias_correction2

                    # checking criteria of switching to SGD training
                    if (
                        state['step'] > 1
                        and corrected_exp_avg.allclose(scaling, rtol=1e-6)
                        and corrected_exp_avg > 0
                    ):
                        group['phase'] = 'SGD'
                        group['lr'] = corrected_exp_avg.item()
        return loss