import math
import torch
from torch.optim import Optimizer
import numpy as np

class AdaX(Optimizer):
    r"""Implements AdaX algorithm.

    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, 1e-4))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-12)
        weight_decay (float, optional): L2 penalty (default: 5e-4)

    .. _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=1.5e-3, betas=(0.9, 1e-4), eps=1e-12,
                 weight_decay=5e-4):
        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)
        super(AdaX, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(AdaX, self).__setstate__(state)

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

                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)


                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                beta1, beta2 = group['betas']
                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad.add_(group['weight_decay'], p.data)
                t = state['step']
                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(1 + beta2).addcmul_(beta2, grad, grad)

                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction2 = ((1 + beta2) ** state['step'] - 1)

                step_size = group['lr'] * math.sqrt(bias_correction2)
                # step_size = group['lr']
                p.data.addcdiv_(-step_size, exp_avg, denom)


        return loss



class AdaXW(Optimizer):
    r"""Implements Adam algorithm.
    It has been proposed in `AdaX: Adaptive Gradient Descent with Exponential Long Term Memory`_.
    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, 1e-4))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (default: 5e-2
    .. _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=0.005, betas=(0.9, 1e-4), eps=1e-12,
                 weight_decay=5e-2):
        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)
        super(AdaXW, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(AdaXW, self).__setstate__(state)

    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:

            beta1, beta2 = group['betas']

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('AdaX does not support sparse gradients, please consider SparseAdam instead')


                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)


                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']


                state['step'] += 1

                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(1 + beta2).addcmul_(beta2, grad, grad)

                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction2 = ((1 + beta2) ** state['step'] - 1)

                step_size = group['lr'] * math.sqrt(bias_correction2)

                p.data.add_(-torch.mul(p.data, group['lr'] * group['weight_decay'])).addcdiv_(-step_size, exp_avg,
                                                                                              denom)

        return loss

