import argparse
import os
from pathlib import Path

import torch
import torch.distributed as dist
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from timm.models.vision_transformer import _cfg, _create_vision_transformer
from torch.optim import Optimizer
from torch.utils.data import DataLoader
from tqdm import tqdm

import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
from colossalai.cluster import DistCoordinator
from colossalai.nn.lr_scheduler import LinearWarmupLR
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device

# ==============================
# Prepare Hyperparameters
# ==============================
NUM_EPOCHS = 60
WARMUP_EPOCHS = 5
LEARNING_RATE = 1e-3


def vit_cifar(**kwargs):
    pretrained_cfg = _cfg(num_classes=10, input_size=(3, 32, 32), crop_pct=1.0)
    model_kwargs = dict(patch_size=4, embed_dim=512, depth=6, num_heads=8, drop_rate=0.1, mlp_ratio=1.0, **kwargs)
    model = _create_vision_transformer("vit_cifar", pretrained_cfg=pretrained_cfg, **model_kwargs)
    return model


def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase):
    # transform
    transform_train = transforms.Compose(
        [
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
        ]
    )
    transform_test = transforms.Compose(
        [
            transforms.Resize(32),
            transforms.ToTensor(),
            transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
        ]
    )

    # CIFAR-10 dataset
    data_path = os.environ.get("DATA", "./data")
    with coordinator.priority_execution():
        train_dataset = torchvision.datasets.CIFAR10(
            root=data_path, train=True, transform=transform_train, download=True
        )
        test_dataset = torchvision.datasets.CIFAR10(
            root=data_path, train=False, transform=transform_test, download=True
        )

    # Data loader
    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
    test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
    return train_dataloader, test_dataloader


@torch.no_grad()
def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float:
    model.eval()
    correct = torch.zeros(1, dtype=torch.int64, device=get_current_device())
    total = torch.zeros(1, dtype=torch.int64, device=get_current_device())
    for images, labels in test_dataloader:
        images = images.cuda()
        labels = labels.cuda()
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    dist.all_reduce(correct)
    dist.all_reduce(total)
    accuracy = correct.item() / total.item()
    if coordinator.is_master():
        print(f"Accuracy of the model on the test images: {accuracy * 100:.2f} %")
    return accuracy


def train_epoch(
    epoch: int,
    model: nn.Module,
    optimizer: Optimizer,
    criterion: nn.Module,
    train_dataloader: DataLoader,
    booster: Booster,
    coordinator: DistCoordinator,
):
    model.train()
    with tqdm(train_dataloader, desc=f"Epoch [{epoch + 1}/{NUM_EPOCHS}]", disable=not coordinator.is_master()) as pbar:
        for images, labels in pbar:
            images = images.cuda()
            labels = labels.cuda()
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)

            # Backward and optimize
            booster.backward(loss, optimizer)
            optimizer.step()
            optimizer.zero_grad()

            # Print log info
            pbar.set_postfix({"loss": loss.item()})


def main():
    # ==============================
    # Parse Arguments
    # ==============================
    parser = argparse.ArgumentParser()
    # FIXME(ver217): gemini is not supported resnet now
    parser.add_argument(
        "-p",
        "--plugin",
        type=str,
        default="torch_ddp",
        choices=["torch_ddp", "torch_ddp_fp16", "low_level_zero"],
        help="plugin to use",
    )
    parser.add_argument("-r", "--resume", type=int, default=-1, help="resume from the epoch's checkpoint")
    parser.add_argument("-c", "--checkpoint", type=str, default="./checkpoint", help="checkpoint directory")
    parser.add_argument("-i", "--interval", type=int, default=5, help="interval of saving checkpoint")
    parser.add_argument(
        "--target_acc", type=float, default=None, help="target accuracy. Raise exception if not reached"
    )
    args = parser.parse_args()

    # ==============================
    # Prepare Checkpoint Directory
    # ==============================
    if args.interval > 0:
        Path(args.checkpoint).mkdir(parents=True, exist_ok=True)

    # ==============================
    # Launch Distributed Environment
    # ==============================
    colossalai.launch_from_torch(config={})
    coordinator = DistCoordinator()

    # update the learning rate with linear scaling
    # old_gpu_num / old_lr = new_gpu_num / new_lr
    global LEARNING_RATE
    LEARNING_RATE *= coordinator.world_size

    # ==============================
    # Instantiate Plugin and Booster
    # ==============================
    booster_kwargs = {}
    if args.plugin == "torch_ddp_fp16":
        booster_kwargs["mixed_precision"] = "fp16"
    if args.plugin.startswith("torch_ddp"):
        plugin = TorchDDPPlugin()
    elif args.plugin == "gemini":
        plugin = GeminiPlugin(placement_policy="static", strict_ddp_mode=True, initial_scale=2**5)
    elif args.plugin == "low_level_zero":
        plugin = LowLevelZeroPlugin(initial_scale=2**5)

    booster = Booster(plugin=plugin, **booster_kwargs)

    # ==============================
    # Prepare Dataloader
    # ==============================
    train_dataloader, test_dataloader = build_dataloader(512, coordinator, plugin)

    # ====================================
    # Prepare model, optimizer, criterion
    # ====================================
    # resent50
    model = torchvision.models.resnet18(num_classes=10)

    # Loss and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE)

    # lr scheduler
    lr_scheduler = LinearWarmupLR(optimizer, NUM_EPOCHS, WARMUP_EPOCHS)

    # ==============================
    # Boost with ColossalAI
    # ==============================
    model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(
        model, optimizer, criterion=criterion, dataloader=train_dataloader, lr_scheduler=lr_scheduler
    )

    # ==============================
    # Resume from checkpoint
    # ==============================
    if args.resume >= 0:
        booster.load_model(model, f"{args.checkpoint}/model_{args.resume}.pth")
        booster.load_optimizer(optimizer, f"{args.checkpoint}/optimizer_{args.resume}.pth")
        booster.load_lr_scheduler(lr_scheduler, f"{args.checkpoint}/lr_scheduler_{args.resume}.pth")

    # ==============================
    # Train model
    # ==============================
    start_epoch = args.resume if args.resume >= 0 else 0
    for epoch in range(start_epoch, NUM_EPOCHS):
        train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator)
        lr_scheduler.step()

        # save checkpoint
        if args.interval > 0 and (epoch + 1) % args.interval == 0:
            booster.save_model(model, f"{args.checkpoint}/model_{epoch + 1}.pth")
            booster.save_optimizer(optimizer, f"{args.checkpoint}/optimizer_{epoch + 1}.pth")
            booster.save_lr_scheduler(lr_scheduler, f"{args.checkpoint}/lr_scheduler_{epoch + 1}.pth")

    accuracy = evaluate(model, test_dataloader, coordinator)
    if args.target_acc is not None:
        assert accuracy >= args.target_acc, f"Accuracy {accuracy} is lower than target accuracy {args.target_acc}"


if __name__ == "__main__":
    main()
