import math
import torch
import typing

__all__ = ['AdaMod']

#NOTE: from https://github.com/jettify/pytorch-optimizer/blob/master/torch_optimizer/adamod.py

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

    - **Paper**: [Adaptive and Momental Bounds for Adaptive Learning Rate Methods](https://arxiv.org/pdf/1910.12249.pdf)
    - **Implementation**: [GitHub @ jettify](https://github.com/jettify/pytorch-optimizer)    

    Arguments:
        params: iterable of parameters to optimize or dicts defining
            parameter groups
        lr: learning rate (default: 1e-3)
        betas: coefficients used for computing running averages of gradient
            and its square (default: (0.9, 0.999))
        beta3: smoothing coefficient for adaptive learning rates
            (default: 0.9999)
        eps: term added to the denominator to improve numerical stability
            (default: 1e-8)
        weight_decay: weight decay (L2 penalty) (default: 0)

    Example:
        >>> import torch_optimizer as optim
        >>> optimizer = optim.AdaMod(model.parameters(), lr=0.1)
        >>> optimizer.zero_grad()
        >>> loss_fn(model(input), target).backward()
        >>> optimizer.step()

    __ https://arxiv.org/abs/1910.12249

    Note:
        Reference code: https://github.com/lancopku/AdaMod
    """

    def __init__(self,
        params: typing.Iterator[torch.nn.Parameter],
        lr: float=1e-3,
        betas: typing.Tuple[float, float]=(0.9, 0.999),
        beta3: float=0.999,
        eps: float=1e-8,
        weight_decay: float=0,
    ) -> None:
        if lr <= 0.0:
            raise ValueError('Invalid learning rate: {}'.format(lr))
        if eps < 0.0:
            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 <= beta3 < 1.0:
            raise ValueError('Invalid beta3 parameter: {}'.format(beta3))
        if weight_decay < 0.0:
            raise ValueError(
                'Invalid weight_decay value: {}'.format(weight_decay)
            )
        defaults = dict(
            lr=lr, betas=betas, beta3=beta3, eps=eps, weight_decay=weight_decay
        )
        super(AdaMod, self).__init__(params, defaults)

    def step(self, closure: typing.Optional[typing.Callable[[], float]]=None) -> typing.Optional[float]:
        """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 p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    msg = 'AdaMod does not support sparse gradients'
                    raise RuntimeError(msg)

                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)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p)
                    # Exponential moving average of actual learning rates
                    state['exp_avg_lr'] = torch.zeros_like(p)

                exp_avg, exp_avg_sq, exp_avg_lr = (
                    state['exp_avg'],
                    state['exp_avg_sq'],
                    state['exp_avg_lr'],
                )
                beta1, beta2 = group['betas']

                state['step'] += 1

                # 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)

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

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = (
                    group['lr']
                    * math.sqrt(bias_correction2)
                    / bias_correction1
                )

                if group['weight_decay'] != 0:
                    p.data.add_(
                        p.data, alpha=-group['weight_decay'] * group['lr']
                    )

                # Applies momental bounds on actual learning rates
                step_size = torch.full_like(denom, step_size)
                step_size.div_(denom)
                exp_avg_lr.mul_(group['beta3']).add_(
                    step_size, alpha=1 - group['beta3']
                )
                step_size = torch.min(step_size, exp_avg_lr)
                step_size.mul_(exp_avg)

                p.data.add_(-step_size)

        return loss