# coding=utf-8
# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
# Modifications Copyright 2022 Amazon.com, Inc. or its affiliates. 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.

"""Learning rate decay functions."""

import math
import smdistributed.modelparallel.torch as smp


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

    def __init__(
        self,
        optimizer,
        start_lr,
        warmup_iter,
        plateau_iter,
        total_iters,
        decay_style,
        last_iter,
        min_lr=0.0,
        use_checkpoint_lr_scheduler=True,
        override_lr_scheduler=False,
    ):
        # Class values.
        self.optimizer = optimizer
        self.start_lr = start_lr
        self.min_lr = min_lr
        self.warmup_iter = warmup_iter
        self.plateau_iter = plateau_iter
        self.num_iters = last_iter
        self.end_iter = total_iters
        assert self.end_iter > 0
        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(self.num_iters)

        if smp.rank() == 0:
            print("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"""

        num_iters_ = min(self.num_iters, self.end_iter - self.warmup_iter)
        # Warmup.
        if self.warmup_iter > 0 and self.num_iters <= self.warmup_iter:
            return float(self.start_lr) * num_iters_ / self.warmup_iter

        num_iters_ = num_iters_ - self.warmup_iter
        if self.decay_style == "linear":
            lr = self.start_lr * (self.end_iter - num_iters_) / self.end_iter
        elif self.decay_style == "plateau":
            if self.num_iters <= self.plateau_iter:
                lr = self.start_lr
            else:
                lr = (
                    self.start_lr
                    * (self.end_iter - self.num_iters)
                    / (self.end_iter - self.plateau_iter)
                )
        elif self.decay_style == "cosine":
            lr = (
                self.start_lr
                / 2.0
                * (math.cos(math.pi * num_iters_ / self.end_iter) + 1)
            )
        elif self.decay_style == "exponential":
            # exp(-0.693) = 1/2
            lr = self.start_lr * math.exp(-0.693 * num_iters_ / self.end_iter)
        else:
            lr = self.start_lr
        return max(lr, self.min_lr)

    def step(self, step_num=None):
        """Set lr for all parameters groups."""
        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):
        state_dict = {
            "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 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:
            if smp.rank() == 0:
                print("Overriding {} value to {}".format(name, cls_value))
            return cls_value

        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)
            )
        if smp.rank() == 0:
            print(" > 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)