# Source: https://github.com/jettify/pytorch-optimizer/blob/master/torch_optimizer/adabelief.py (apache 2.0)

import math

import torch
from torch.optim.optimizer import Optimizer

from .a2grad import Betas2, OptFloat, OptLossClosure, Params

the belief in observed gradients__.

Arguments:
params: iterable of parameters to optimize or dicts defining
parameter groups
lr: learning rate (default: 1e-2)
betas: coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps: term added to the denominator to improve
numerical stability (default: 0.001)
weight_decay: weight decay (L2 penalty) (default: 0)
algorithm from the paper On the Convergence of Adam and Beyond_
(default: False)
weight_decouple:  If set as True, then the optimizer uses decoupled
weight decay as in AdamW (default: False)
fixed_decay : This is used when
weight_decouple is set as True.
When fixed_decay == True, the weight decay is performed as
$W_{new} = W_{old} - W_{old} \times decay$.
When fixed_decay == False, the weight decay is performed as
$W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in
this case, the weight decay ratio decreases with learning
rate (lr).  (default: False)
rectify: (default: False) If set as True, then perform the rectified

__ https://arxiv.org/abs/2010.07468

Note:
"""

def __init__(
self,
params: Params,
lr: float = 1e-3,
betas: Betas2 = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0,
weight_decouple: bool = False,
fixed_decay: bool = False,
rectify: bool = False,
) -> 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 weight_decay < 0:
raise ValueError(
'Invalid weight_decay value: {}'.format(weight_decay)
)
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
)

self._weight_decouple = weight_decouple
self._rectify = rectify
self._fixed_decay = fixed_decay

def __setstate__(self, state):
for group in self.param_groups:

[docs]    def step(self, closure: OptLossClosure = None) -> OptFloat:
r"""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']:
continue
raise RuntimeError(
)

state = self.state[p]

beta1, beta2 = group['betas']

# State initialization
if len(state) == 0:
state['rho_inf'] = 2.0 / (1.0 - beta2) - 1.0
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state['exp_avg_var'] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# Maintains max of all exp. moving avg. of
state['max_exp_avg_var'] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)

# get current state variable
exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']

state['step'] += 1
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']

# perform weight decay, check if decoupled weight decay
if self._weight_decouple:
if not self._fixed_decay:
p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
else:
p.data.mul_(1.0 - group['weight_decay'])
else:
if group['weight_decay'] != 0:

# Update first and second moment running average
)

max_exp_avg_var = state['max_exp_avg_var']
# Maintains the maximum of all 2nd moment running
# avg. till now
torch.max(
max_exp_avg_var, exp_avg_var, out=max_exp_avg_var
)

# Use the max. for normalizing running avg. of gradient
denom = (
/ math.sqrt(bias_correction2)
else:
denom = (
/ math.sqrt(bias_correction2)

if not self._rectify:
# Default update
step_size = group['lr'] / bias_correction1

else:  # Rectified update
# calculate rho_t
state['rho_t'] = state['rho_inf'] - 2 * state[
'step'
] * beta2 ** state['step'] / (1.0 - beta2 ** state['step'])

if (
state['rho_t'] > 4
):  # perform Adam style update if variance is small
rho_inf, rho_t = state['rho_inf'], state['rho_t']
rt = (
(rho_t - 4.0)
* (rho_t - 2.0)
* rho_inf
/ (rho_inf - 4.0)
/ (rho_inf - 2.0)
/ rho_t
)
rt = math.sqrt(rt)

step_size = rt * group['lr'] / bias_correction1