import torch
from torch.optim import Optimizer


class Nadam(Optimizer):
    """Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).

    It has been proposed in `Incorporating Nesterov Momentum into Adam`__.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 2e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square
        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)
        schedule_decay (float, optional): momentum schedule decay (default: 4e-3)

    __ http://cs229.stanford.edu/proj2015/054_report.pdf
    __ http://www.cs.toronto.edu/~fritz/absps/momentum.pdf

        Originally taken from: https://github.com/pytorch/pytorch/pull/1408
        NOTE: Has potential issues but does work well on some problems.
    """

    def __init__(
        self,
        params,
        lr=2e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        schedule_decay=4e-3,
    ):
        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            schedule_decay=schedule_decay,
        )
        super(Nadam, self).__init__(params, defaults)

    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:
            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data
                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    state["m_schedule"] = 1.0
                    state["exp_avg"] = grad.new().resize_as_(grad).zero_()
                    state["exp_avg_sq"] = grad.new().resize_as_(grad).zero_()

                # Warming momentum schedule
                m_schedule = state["m_schedule"]
                schedule_decay = group["schedule_decay"]
                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                beta1, beta2 = group["betas"]
                eps = group["eps"]
                state["step"] += 1
                t = state["step"]

                if group["weight_decay"] != 0:
                    grad = grad.add(group["weight_decay"], p.data)

                momentum_cache_t = beta1 * (1.0 - 0.5 * (0.96 ** (t * schedule_decay)))
                momentum_cache_t_1 = beta1 * (
                    1.0 - 0.5 * (0.96 ** ((t + 1) * schedule_decay))
                )
                m_schedule_new = m_schedule * momentum_cache_t
                m_schedule_next = m_schedule * momentum_cache_t * momentum_cache_t_1
                state["m_schedule"] = m_schedule_new

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
                exp_avg_sq_prime = exp_avg_sq / (1.0 - beta2 ** t)
                denom = exp_avg_sq_prime.sqrt_().add_(eps)

                p.data.addcdiv_(
                    -group["lr"] * (1.0 - momentum_cache_t) / (1.0 - m_schedule_new),
                    grad,
                    denom,
                )
                p.data.addcdiv_(
                    -group["lr"] * momentum_cache_t_1 / (1.0 - m_schedule_next),
                    exp_avg,
                    denom,
                )

        return loss