# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch DataLoader for TFRecords"""

import torch
from torch.optim.lr_scheduler import _LRScheduler
import math

from utils import print_rank_0


class AnnealingLR(_LRScheduler):
    """Anneals the learning rate"""

    DECAY_STYLES = ['linear', 'cosine', 'constant', 'None']

    def __init__(self, optimizer, start_lr, warmup_iter, num_iters,
                 decay_style=None, last_iter=-1, min_lr=0.0,
                 use_checkpoint_lr_scheduler=True,
                 override_lr_scheduler=False):
        self.optimizer = optimizer
        self.start_lr = start_lr
        self.min_lr = min_lr
        self.warmup_iter = warmup_iter
        self.num_iters = last_iter + 1
        self.end_iter = num_iters
        self.decay_style = decay_style.lower() if isinstance(decay_style, str) \
                           else None
        self.override_lr_scheduler = override_lr_scheduler
        self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler
        if self.override_lr_scheduler:
            assert not self.use_checkpoint_lr_scheduler, 'both override and '\
                'use-checkpoint are set.'
        self.step(self.num_iters)
        if torch.distributed.get_rank() == 0:
            print('learning rate decaying', decay_style)

    def get_lr(self):
        # https://openreview.net/pdf?id=BJYwwY9ll pg. 4
        num_iters_ = min(self.num_iters, self.end_iter - self.warmup_iter)
        if self.warmup_iter > 0 and self.num_iters <= self.warmup_iter:
            return float(self.start_lr) * num_iters_ / self.warmup_iter
        else:
            if self.decay_style == self.DECAY_STYLES[0]:
                lr = self.start_lr * ((self.end_iter - (num_iters_ - self.warmup_iter)) / self.end_iter)
            elif self.decay_style == self.DECAY_STYLES[1]:
                lr = self.start_lr / 2.0 * (math.cos(math.pi * (num_iters_ - self.warmup_iter) / self.end_iter) + 1)
            else:
                lr = self.start_lr
            return max(lr, self.min_lr)

    def step(self, step_num=None):
        if step_num is None:
            step_num = self.num_iters + 1
        self.num_iters = step_num
        new_lr = self.get_lr()
        for group in self.optimizer.param_groups:
            group['lr'] = new_lr

    def state_dict(self):
        sd = {
                'start_lr': self.start_lr,
                'warmup_iter': self.warmup_iter,
                'num_iters': self.num_iters,
                'decay_style': self.decay_style,
                'end_iter': self.end_iter,
                'min_lr': self.min_lr
        }
        return sd


    def check_and_set_(self, cls_value, sd_value, name):
        if self.override_lr_scheduler:
            print_rank_0(' > overriding {} value to {}'.format(name, cls_value))
            return cls_value
        else:
            if not self.use_checkpoint_lr_scheduler:
                assert cls_value == sd_value, 'AnnealingLR: class input value' \
                    'and checkpoint values for {} do not match'.format(name)
            print_rank_0(' > using checkpoint value {} for {}'.format(sd_value,
                                                                      name))
            return sd_value

    def load_state_dict(self, sd):

        self.start_lr = self.check_and_set_(self.start_lr, sd['start_lr'],
                                            'learning rate')
        self.min_lr = self.check_and_set_(self.min_lr, sd['min_lr'],
                                          'minimum learning rate')
        self.warmup_iter = self.check_and_set_(self.warmup_iter,
                                               sd['warmup_iter'],
                                               'warmup iterations')
        self.end_iter = self.check_and_set_(self.end_iter, sd['end_iter'],
                                            'total number of iterations')
        self.decay_style = self.check_and_set_(self.decay_style,
                                               sd['decay_style'],
                                               'decay style')

        self.num_iters = sd['num_iters']
        self.step(self.num_iters)
