"""Wrappers for training optimizers."""
import math
import torch
from tensorflow import keras


def get_optimizer(framework, config):
    """Get the optimizer specified in config for model training.

    Arguments
    ---------
    framework : str
        Name of the deep learning framework used. Current options are
        ``['torch', 'keras']``.
    config : dict
        The config dict generated from the YAML config file.

    Returns
    -------
    An optimizer object for the specified deep learning framework.
    """

    if config['training']['optimizer'] is None:
        raise ValueError('An optimizer must be specified in the config '
                         'file.')

    if framework in ['torch', 'pytorch']:
        return torch_optimizers.get(config['training']['optimizer'].lower())
    elif framework == 'keras':
        return keras_optimizers.get(config['training']['optimizer'].lower())


class TorchAdamW(torch.optim.Optimizer):
    """AdamW algorithm as implemented in `Torch_AdamW`_.

    The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
    The AdamW variant was 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 coefficient (default: 1e-2)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False)
    .. _Torch_AdamW: https://github.com/pytorch/pytorch/pull/3740
    .. _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, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=1e-2, 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]))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(TorchAdamW, self).__init__(params, defaults)

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

    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

                # Perform stepweight decay
                p.data.mul_(1 - group['lr'] * group['weight_decay'])

                # Perform optimization step
                grad = p.grad.data
                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.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)

                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

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                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'] * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)

        return loss


torch_optimizers = {
    'adadelta': torch.optim.Adadelta,
    'adam': torch.optim.Adam,
    'adamw': TorchAdamW,
    'sparseadam': torch.optim.SparseAdam,
    'adamax': torch.optim.Adamax,
    'asgd': torch.optim.ASGD,
    'rmsprop': torch.optim.RMSprop,
    'sgd': torch.optim.SGD,
}

keras_optimizers = {
    'adadelta': keras.optimizers.Adadelta,
    'adagrad': keras.optimizers.Adagrad,
    'adam': keras.optimizers.Adam,
    'adamax': keras.optimizers.Adamax,
    'nadam': keras.optimizers.Nadam,
    'rmsprop': keras.optimizers.RMSprop,
    'sgd': keras.optimizers.SGD
}
