# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

import math
import types

import torch
import torch.optim
# from ipdb import set_trace
from fairseq.optim import FairseqOptimizer, register_optimizer

# from tensorboardX import SummaryWriter
# # writer = SummaryWriter(logdir='./log/wmt/')
# writer = SummaryWriter(logdir='./log/ada/')
# iter_idx = 0

@register_optimizer('radam')
class FairseqRAdam(FairseqOptimizer):

    def __init__(self, args, params):
        super().__init__(args)
        self.args = args

        self._optimizer = RAdam(params, **self.optimizer_config)
        self._optimizer.name = args.tb_tag + '_' + self._optimizer.name

    @staticmethod
    def add_args(parser):
        """Add optimizer-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('--adam-betas', default='(0.9, 0.999)', metavar='B',
                            help='betas for Adam optimizer')
        parser.add_argument('--adam-eps', type=float, default=1e-8, metavar='D',
                            help='epsilon for Adam optimizer')
        parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
                            help='weight decay')
        parser.add_argument('--tb-tag', default="", type=str,
                            help='tb tag')
        # fmt: on

    @property
    def optimizer_config(self):
        """
        Return a kwarg dictionary that will be used to override optimizer
        args stored in checkpoints. This allows us to load a checkpoint and
        resume training using a different set of optimizer args, e.g., with a
        different learning rate.
        """
        return {
            'lr': self.args.lr[0],
            'betas': eval(self.args.adam_betas),
            'eps': self.args.adam_eps,
            'weight_decay': self.args.weight_decay,
        }

class RAdam(torch.optim.Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, amsgrad=False):
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)

        self.name = '{}_{}_{}'.format(lr, betas[0], betas[1])
        super(RAdam, self).__init__(params, defaults)

    @property
    def supports_memory_efficient_fp16(self):
        return True

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        # global iter_idx
        # iter_idx += 1
        grad_list = list()
        mom_list = list()
        mom_2rd_list = list()
        assert 'adam_1k' not in self.name
        # writer_iter = iter_idx

        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.float()
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                amsgrad = group['amsgrad']

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)

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

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

                state['step'] += 1

                beta2_t = beta2 ** state['step']
                N_sma_max = 2 / (1 - beta2) - 1
                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)

                if group['weight_decay'] != 0:
                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    step_size = group['lr'] * math.sqrt((1 - beta2_t ) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) * (N_sma_max) / N_sma / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                else:
                    step_size = group['lr'] / (1 - beta1 ** state['step'])
                    p_data_fp32.add_(-step_size, exp_avg)

                p.data.copy_(p_data_fp32)

        #         if writer_iter > 0 and writer_iter % 300 == 0 or writer_iter in [1, 5, 10, 25, 50, 75, 100, 150, 200]:
        #             grad_list.extend( grad.abs().add_(1e-9).log().view(-1).tolist()  )
        #             mom_list.extend( exp_avg.abs().add_(1e-9).log().view(-1).tolist() )
        #             mom_2rd_list.extend( exp_avg_sq.abs().add_(1e-9).log().view(-1).tolist() )

        # if writer_iter > 0 and writer_iter % 300 == 0 or writer_iter in [1, 5, 10, 25, 50, 75, 100, 150, 200]:
        #     writer.add_histogram('grad/{}'.format(self.name), grad_list, writer_iter)
        #     writer.add_histogram('mom/{}'.format(self.name), mom_list, writer_iter)
        #     writer.add_histogram('mom_sq/{}'.format(self.name), mom_2rd_list, writer_iter)

        return loss
