# coding=utf-8
# Copyright (c) 2020-2022, 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
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"""Learning rate decay functions."""

import math

from megatron import print_rank_0

class AnnealingLR(object):
    """Anneals the learning rate."""

    def __init__(self, optimizer, max_lr, min_lr,
                 warmup_steps, decay_steps, decay_style,
                 use_checkpoint_lr_scheduler=True,
                 override_lr_scheduler=False):

        # Class values.
        self.optimizer = optimizer

        self.max_lr = float(max_lr)
        self.min_lr = min_lr
        assert self.min_lr >= 0.0
        assert self.max_lr >= self.min_lr

        self.warmup_steps = warmup_steps
        self.num_steps = 0
        self.decay_steps = decay_steps
        assert self.decay_steps > 0
        assert self.warmup_steps < self.decay_steps

        self.decay_style = decay_style

        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.'

        # Set the learning rate
        self.step(0)

        print_rank_0('> learning rate decay style: {}'.format(self.decay_style))


    def get_lr(self):
        """Learning rate decay functions from:
              https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""

        # Use linear warmup for the initial part.
        if self.warmup_steps > 0 and self.num_steps <= self.warmup_steps:
            return self.max_lr * float(self.num_steps) / \
                float(self.warmup_steps)

        # If the learning rate is constant, just return the initial value.
        if self.decay_style == 'constant':
            return self.max_lr

        # For any steps larger than `self.decay_steps`, use `self.min_lr`.
        if self.num_steps > self.decay_steps:
            return self.min_lr
        
        # If we are done with the warmup period, use the decay style.
        num_steps_ = self.num_steps - self.warmup_steps
        decay_steps_ = self.decay_steps - self.warmup_steps
        decay_ratio = float(num_steps_) / float(decay_steps_)
        assert decay_ratio >= 0.0
        assert decay_ratio <= 1.0
        delta_lr = self.max_lr - self.min_lr

        if self.decay_style == 'linear':
            coeff = (1.0 - decay_ratio)
        elif self.decay_style == 'cosine':
            coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
        else:
            raise Exception('{} decay style is not supported.'.format(
                self.decay_style))

        return self.min_lr + coeff * delta_lr


    def step(self, increment):
        """Set lr for all parameters groups."""
        self.num_steps += increment
        new_lr = self.get_lr()
        for group in self.optimizer.param_groups:
            group['lr'] = new_lr


    def state_dict(self):
        state_dict = {
            'max_lr': self.max_lr,
            'warmup_steps': self.warmup_steps,
            'num_steps': self.num_steps,
            'decay_style': self.decay_style,
            'decay_steps': self.decay_steps,
            'min_lr': self.min_lr
        }
        return state_dict


    def _check_and_set(self, cls_value, sd_value, name):
        """Auxiliary function for checking the values in the checkpoint and
        setting them."""
        if self.override_lr_scheduler:
            print_rank_0(' > overriding {} value to {}'.format(name, cls_value))
            return cls_value

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


    def load_state_dict(self, sd):

        if 'start_lr' in sd:
            max_lr_ = sd['start_lr']
        else:
            max_lr_ = sd['max_lr']
        self.max_lr = self._check_and_set(self.max_lr, max_lr_,
                                          'learning rate')
        
        self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'],
                                          'minimum learning rate')

        if 'warmup_iter' in sd:
            warmup_steps_ = sd['warmup_iter']
        else:
            warmup_steps_ = sd['warmup_steps']
        self.warmup_steps = self._check_and_set(self.warmup_steps,
                                                warmup_steps_,
                                                'warmup iterations')

        if 'end_iter' in sd:
            decay_steps_ = sd['end_iter']
        else:
            decay_steps_ = sd['decay_steps']
        self.decay_steps = self._check_and_set(self.decay_steps, decay_steps_,
                                               'total number of iterations')
        self.decay_style = self._check_and_set(self.decay_style,
                                               sd['decay_style'],
                                               'decay style')

        if 'num_iters' in sd:
            num_steps = sd['num_iters']
        else:
            num_steps = sd['num_steps']
        self.step(increment=num_steps)
