# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from dataclasses import dataclass, field
from typing import List

from fairseq.dataclass import FairseqDataclass
from omegaconf import II

from . import FairseqLRScheduler, register_lr_scheduler


@dataclass
class InverseSquareRootScheduleConfig(FairseqDataclass):
    warmup_updates: int = field(
        default=4000,
        metadata={"help": "warmup the learning rate linearly for the first N updates"},
    )
    warmup_init_lr: float = field(
        default=-1,
        metadata={
            "help": "initial learning rate during warmup phase; default is args.lr"
        },
    )
    # TODO common vars at parent class
    lr: List[float] = II("params.optimization.lr")


@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootScheduleConfig)
class InverseSquareRootSchedule(FairseqLRScheduler):
    """Decay the LR based on the inverse square root of the update number.

    We also support a warmup phase where we linearly increase the learning rate
    from some initial learning rate (``--warmup-init-lr``) until the configured
    learning rate (``--lr``). Thereafter we decay proportional to the number of
    updates, with a decay factor set to align with the configured learning rate.

    During warmup::

      lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates)
      lr = lrs[update_num]

    After warmup::

      decay_factor = args.lr * sqrt(args.warmup_updates)
      lr = decay_factor / sqrt(update_num)
    """

    def __init__(self, args, optimizer):
        super().__init__(args, optimizer)
        if len(args.lr) > 1:
            raise ValueError(
                "Cannot use a fixed learning rate schedule with inverse_sqrt."
                " Consider --lr-scheduler=fixed instead."
            )
        warmup_end_lr = args.lr[0]
        if args.warmup_init_lr < 0:
            args.warmup_init_lr = 0 if args.warmup_updates > 0 else warmup_end_lr

        # linearly warmup for the first args.warmup_updates
        self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates

        # then, decay prop. to the inverse square root of the update number
        self.decay_factor = warmup_end_lr * args.warmup_updates ** 0.5

        # initial learning rate
        self.lr = args.warmup_init_lr
        self.optimizer.set_lr(self.lr)

    def step(self, epoch, val_loss=None):
        """Update the learning rate at the end of the given epoch."""
        super().step(epoch, val_loss)
        # we don't change the learning rate at epoch boundaries
        return self.optimizer.get_lr()

    def step_update(self, num_updates):
        """Update the learning rate after each update."""
        if num_updates < self.args.warmup_updates:
            self.lr = self.args.warmup_init_lr + num_updates * self.lr_step
        else:
            self.lr = self.decay_factor * num_updates ** -0.5
        self.optimizer.set_lr(self.lr)
        return self.lr
