# Copyright 2020 - 2021 MONAI Consortium
# 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.

import math
import warnings
from typing import List

from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
from torch import nn as nn
from torch.optim import Adam, Optimizer
from torch.optim.lr_scheduler import _LRScheduler

__all__ = ["LinearLR", "ExponentialLR"]


class _LRSchedulerMONAI(_LRScheduler):
    """Base class for increasing the learning rate between two boundaries over a number
    of iterations"""

    def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None:
        """
        Args:
            optimizer: wrapped optimizer.
            end_lr: the final learning rate.
            num_iter: the number of iterations over which the test occurs.
            last_epoch: the index of last epoch.
        Returns:
            None
        """
        self.end_lr = end_lr
        self.num_iter = num_iter
        super(_LRSchedulerMONAI, self).__init__(optimizer, last_epoch)


class LinearLR(_LRSchedulerMONAI):
    """Linearly increases the learning rate between two boundaries over a number of
    iterations.
    """

    def get_lr(self):
        r = self.last_epoch / (self.num_iter - 1)
        return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]


class ExponentialLR(_LRSchedulerMONAI):
    """Exponentially increases the learning rate between two boundaries over a number of
    iterations.
    """

    def get_lr(self):
        r = self.last_epoch / (self.num_iter - 1)
        return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]


class WarmupCosineSchedule(LambdaLR):
    """Linear warmup and then cosine decay.
    Based on https://huggingface.co/ implementation.
    """

    def __init__(
            self, optimizer: Optimizer, warmup_steps: int, t_total: int, cycles: float = 0.5, last_epoch: int = -1
    ) -> None:
        """
        Args:
            optimizer: wrapped optimizer.
            warmup_steps: number of warmup iterations.
            t_total: total number of training iterations.
            cycles: cosine cycles parameter.
            last_epoch: the index of last epoch.
        Returns:
            None
        """
        self.warmup_steps = warmup_steps
        self.t_total = t_total
        self.cycles = cycles
        super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch)

    def lr_lambda(self, step):
        if step < self.warmup_steps:
            return float(step) / float(max(1.0, self.warmup_steps))
        progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))


class LinearWarmupCosineAnnealingLR(_LRScheduler):

    def __init__(
            self,
            optimizer: Optimizer,
            warmup_epochs: int,
            max_epochs: int,
            warmup_start_lr: float = 0.0,
            eta_min: float = 0.0,
            last_epoch: int = -1,
    ) -> None:
        """
        Args:
            optimizer (Optimizer): Wrapped optimizer.
            warmup_epochs (int): Maximum number of iterations for linear warmup
            max_epochs (int): Maximum number of iterations
            warmup_start_lr (float): Learning rate to start the linear warmup. Default: 0.
            eta_min (float): Minimum learning rate. Default: 0.
            last_epoch (int): The index of last epoch. Default: -1.
        """
        self.warmup_epochs = warmup_epochs
        self.max_epochs = max_epochs
        self.warmup_start_lr = warmup_start_lr
        self.eta_min = eta_min

        super(LinearWarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        """
        Compute learning rate using chainable form of the scheduler
        """
        if not self._get_lr_called_within_step:
            warnings.warn(
                "To get the last learning rate computed by the scheduler, "
                "please use `get_last_lr()`.",
                UserWarning,
            )

        if self.last_epoch == 0:
            return [self.warmup_start_lr] * len(self.base_lrs)
        elif self.last_epoch < self.warmup_epochs:
            return [
                group["lr"] + (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
            ]
        elif self.last_epoch == self.warmup_epochs:
            return self.base_lrs
        elif (self.last_epoch - 1 - self.max_epochs) % (2 * (self.max_epochs - self.warmup_epochs)) == 0:
            return [
                group["lr"] + (base_lr - self.eta_min) *
                (1 - math.cos(math.pi / (self.max_epochs - self.warmup_epochs))) / 2
                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
            ]

        return [
            (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs))) /
            (
                    1 +
                    math.cos(
                        math.pi * (self.last_epoch - self.warmup_epochs - 1) / (self.max_epochs - self.warmup_epochs))
            ) * (group["lr"] - self.eta_min) + self.eta_min for group in self.optimizer.param_groups
        ]

    def _get_closed_form_lr(self) -> List[float]:
        """
        Called when epoch is passed as a param to the `step` function of the scheduler.
        """
        if self.last_epoch < self.warmup_epochs:
            return [
                self.warmup_start_lr + self.last_epoch * (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
                for base_lr in self.base_lrs
            ]

        return [
            self.eta_min + 0.5 * (base_lr - self.eta_min) *
            (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs)))
            for base_lr in self.base_lrs
        ]
