# This example requires the following dependencies to be installed:
# pip install lightly

# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.

import copy

import pytorch_lightning as pl
import torch
import torchvision
from torch import nn

from lightly.loss import NegativeCosineSimilarity
from lightly.models.modules import BYOLPredictionHead, BYOLProjectionHead
from lightly.models.utils import deactivate_requires_grad, update_momentum
from lightly.transforms.byol_transform import (
    BYOLTransform,
    BYOLView1Transform,
    BYOLView2Transform,
)
from lightly.utils.scheduler import cosine_schedule


class BYOL(pl.LightningModule):
    def __init__(self):
        super().__init__()
        resnet = torchvision.models.resnet18()
        self.backbone = nn.Sequential(*list(resnet.children())[:-1])
        self.projection_head = BYOLProjectionHead(512, 1024, 256)
        self.prediction_head = BYOLPredictionHead(256, 1024, 256)

        self.backbone_momentum = copy.deepcopy(self.backbone)
        self.projection_head_momentum = copy.deepcopy(self.projection_head)

        deactivate_requires_grad(self.backbone_momentum)
        deactivate_requires_grad(self.projection_head_momentum)

        self.criterion = NegativeCosineSimilarity()

    def forward(self, x):
        y = self.backbone(x).flatten(start_dim=1)
        z = self.projection_head(y)
        p = self.prediction_head(z)
        return p

    def forward_momentum(self, x):
        y = self.backbone_momentum(x).flatten(start_dim=1)
        z = self.projection_head_momentum(y)
        z = z.detach()
        return z

    def training_step(self, batch, batch_idx):
        momentum = cosine_schedule(self.current_epoch, 10, 0.996, 1)
        update_momentum(self.backbone, self.backbone_momentum, m=momentum)
        update_momentum(self.projection_head, self.projection_head_momentum, m=momentum)
        (x0, x1) = batch[0]
        p0 = self.forward(x0)
        z0 = self.forward_momentum(x0)
        p1 = self.forward(x1)
        z1 = self.forward_momentum(x1)
        loss = 0.5 * (self.criterion(p0, z1) + self.criterion(p1, z0))
        return loss

    def configure_optimizers(self):
        return torch.optim.SGD(self.parameters(), lr=0.06)


model = BYOL()

# We disable resizing and gaussian blur for cifar10.
transform = BYOLTransform(
    view_1_transform=BYOLView1Transform(input_size=32, gaussian_blur=0.0),
    view_2_transform=BYOLView2Transform(input_size=32, gaussian_blur=0.0),
)
dataset = torchvision.datasets.CIFAR10(
    "datasets/cifar10", download=True, transform=transform
)
# or create a dataset from a folder containing images or videos:
# dataset = LightlyDataset("path/to/folder", transform=transform)

dataloader = torch.utils.data.DataLoader(
    dataset,
    batch_size=256,
    shuffle=True,
    drop_last=True,
    num_workers=8,
)

accelerator = "gpu" if torch.cuda.is_available() else "cpu"

trainer = pl.Trainer(max_epochs=10, devices=1, accelerator=accelerator)
trainer.fit(model=model, train_dataloaders=dataloader)