# Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import List
import torch

from annotator.oneformer.detectron2.solver.lr_scheduler import LRScheduler, _get_warmup_factor_at_iter

# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
# only on epoch boundaries. We typically use iteration based schedules instead.
# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean
# "iteration" instead.

# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating
# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it.


class WarmupPolyLR(LRScheduler):
    """
    Poly learning rate schedule used to train DeepLab.
    Paper: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
        Atrous Convolution, and Fully Connected CRFs.
    Reference: https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/utils/train_utils.py#L337  # noqa
    """

    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        max_iters: int,
        warmup_factor: float = 0.001,
        warmup_iters: int = 1000,
        warmup_method: str = "linear",
        last_epoch: int = -1,
        power: float = 0.9,
        constant_ending: float = 0.0,
    ):
        self.max_iters = max_iters
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        self.power = power
        self.constant_ending = constant_ending
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        warmup_factor = _get_warmup_factor_at_iter(
            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
        )
        if self.constant_ending > 0 and warmup_factor == 1.0:
            # Constant ending lr.
            if (
                math.pow((1.0 - self.last_epoch / self.max_iters), self.power)
                < self.constant_ending
            ):
                return [base_lr * self.constant_ending for base_lr in self.base_lrs]
        return [
            base_lr * warmup_factor * math.pow((1.0 - self.last_epoch / self.max_iters), self.power)
            for base_lr in self.base_lrs
        ]

    def _compute_values(self) -> List[float]:
        # The new interface
        return self.get_lr()
