"""
AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""

import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer
import math

class SGDP(Optimizer):
    def __init__(self, params, lr, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
        defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
                        nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)
        super(SGDP, self).__init__(params, defaults)

    def _channel_view(self, x):
        return x.view(x.size(0), -1)

    def _layer_view(self, x):
        return x.view(1, -1)

    def _cosine_similarity(self, x, y, eps, view_func):
        x = view_func(x)
        y = view_func(y)

        x_norm = x.norm(dim=1).add_(eps)
        y_norm = y.norm(dim=1).add_(eps)
        dot = (x * y).sum(dim=1)

        return dot.abs() / x_norm / y_norm

    def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
        wd = 1
        expand_size = [-1] + [1] * (len(p.shape) - 1)
        for view_func in [self._channel_view, self._layer_view]:

            cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)

            if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
                p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
                perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
                wd = wd_ratio

                return perturb, wd

        return perturb, wd

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']

            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['momentum'] = torch.zeros_like(p.data)

                # SGD
                buf = state['momentum']
                buf.mul_(momentum).add_(1 - dampening, grad)
                if nesterov:
                    d_p = grad + momentum * buf
                else:
                    d_p = buf

                # Projection
                wd_ratio = 1
                if len(p.shape) > 1:
                    d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])

                # Weight decay
                if weight_decay != 0:
                    p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))

                # Step
                p.data.add_(-group['lr'], d_p)

        return loss
