import dataclasses
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
import os

try:
    import pickle
    import random
    from pathlib import Path
    from PIL import Image, ImageFilter
    import timm
    from tqdm import tqdm
    import torch
    import torch.backends.cudnn as cudnn
    import torch.distributed as dist
    import torch.multiprocessing as mp
    import torch.nn.parallel
    import torch.optim
    import torch.utils.data
    import torch.utils.data.distributed
    import torchvision.transforms as transforms
    from augly.image import (EncodingQuality, OneOf,
                             RandomBlur, RandomEmojiOverlay, RandomPixelization,
                             RandomRotation, ShufflePixels)
    from augly.image.functional import overlay_emoji, overlay_image, overlay_text
    from augly.image.transforms import BaseTransform
    from augly.utils import pathmgr
    from augly.utils.base_paths import MODULE_BASE_DIR
    from augly.utils.constants import FONT_LIST_PATH, FONTS_DIR, SMILEY_EMOJI_DIR

    from pytorch_metric_learning import losses
    from pytorch_metric_learning.utils import distributed as pml_dist


except:
    pass


def dataclass_from_dict(klass, d):
    try:
        fieldtypes = {f.name: f.type for f in dataclasses.fields(klass)}
        return klass(**{f: dataclass_from_dict(fieldtypes[f], d[f]) for f in d})
    except:
        return d  # Not a dataclass field


@dataclass
class TrainingArguments:
    output_dir: Optional[str] = field(
        default='./output', metadata={"help": "output checkpoint saving dir."}
    )
    distributed: Optional[bool] = field(
        default=False, metadata={"help": "If true, use all gpu in your machine, else use only one gpu."}
    )
    gpu: Optional[int] = field(
        default=0, metadata={"help": "When distributed is False, use this gpu No. in your machine."}
    )
    start_epoch: Optional[int] = field(
        default=0, metadata={"help": "Start epoch number."}
    )
    epochs: Optional[int] = field(
        default=6, metadata={"help": "End epoch number."}
    )
    batch_size: Optional[int] = field(
        default=128, metadata={"help": "Total batch size in all gpu."}
    )
    init_lr: Optional[float] = field(
        default=0.1, metadata={"help": "init learning rate in SGD."}
    )
    train_data_dir: Optional[str] = field(default=None,
                                          metadata={"help": "The dir containing all training data image files."})


def train_isc(model, training_args):
    from towhee.trainer.training_config import get_dataclasses_help
    print('**** TrainingArguments ****')
    get_dataclasses_help(TrainingArguments)
    training_args = dataclass_from_dict(TrainingArguments, training_args)
    if training_args.distributed is True:
        ngpus_per_node = torch.cuda.device_count()
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, model, training_args))
    else:
        main_worker(training_args.gpu, 1, model, training_args)


def main_worker(gpu, ngpus_per_node, model, training_args):
    rank = gpu
    world_size = ngpus_per_node
    distributed = training_args.distributed
    if distributed:
        dist.init_process_group(backend='nccl', init_method='tcp://localhost:10001',
                                world_size=world_size, rank=rank)
        torch.distributed.barrier(device_ids=[rank])

    # infer learning rate before changing batch size
    init_lr = training_args.init_lr

    if distributed:
        # Apply SyncBN
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
    torch.cuda.set_device(gpu)
    model.cuda(gpu)
    batch_size = training_args.batch_size
    batch_size = int(batch_size / ngpus_per_node)
    workers = 8
    if distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu])

    memory_size = 20000
    pos_margin = 0.0
    neg_margin = 1.0
    loss_fn = losses.ContrastiveLoss(pos_margin=pos_margin, neg_margin=neg_margin)
    loss_fn = losses.CrossBatchMemory(loss_fn, embedding_size=256, memory_size=memory_size)
    loss_fn = pml_dist.DistributedLossWrapper(loss=loss_fn, device_ids=[rank])

    decay = []
    no_decay = []
    for name, param in model.named_parameters():
        if not param.requires_grad:
            continue  # frozen weights
        if len(param.shape) == 1 or name.endswith(".bias") or "gain" in name:
            no_decay.append(param)
        else:
            decay.append(param)

    optim_params = [
        {'params': no_decay, 'weight_decay': 0.},
        {'params': decay, 'weight_decay': 1e-6}
    ]
    momentum = 0.9

    optimizer = torch.optim.SGD(optim_params, init_lr, momentum=momentum)
    scaler = torch.cuda.amp.GradScaler()

    cudnn.benchmark = True

    train_dataset = get_dataset(training_args.train_data_dir)
    if distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=batch_size, shuffle=(train_sampler is None),
        num_workers=workers, pin_memory=True, sampler=train_sampler, drop_last=True)
    start_epoch = training_args.start_epoch
    epochs = training_args.epochs
    for epoch in range(start_epoch, epochs):
        if distributed:
            train_sampler.set_epoch(epoch)
        train_one_epoch(train_loader, model, loss_fn, optimizer, scaler, epoch, rank)

        if not distributed or (distributed and rank == 0):
            if not os.path.exists(training_args.output_dir):
                os.mkdir(training_args.output_dir)
            torch.save({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'optimizer': optimizer.state_dict(),
                'training_args': training_args,
            }, f'{training_args.output_dir}/checkpoint_epoch{epoch:04d}.pth.tar')


def train_one_epoch(train_loader, model, loss_fn, optimizer, scaler, epoch, rank):
    losses = AverageMeter('Loss', ':.4f')
    progress = tqdm(train_loader, desc=f'epoch {epoch}', leave=False, total=len(train_loader))

    model.train()

    for labels, images in progress:
        optimizer.zero_grad()

        labels = labels.cuda(rank, non_blocking=True)
        images = torch.cat([
            image for image in images
        ], dim=0).cuda(rank, non_blocking=True)
        labels = torch.tile(labels, dims=(2,))

        with torch.cuda.amp.autocast():
            embeddings = model(images)
            loss = loss_fn(embeddings, labels)

        losses.update(loss.item(), images.size(0))

        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

        progress.set_postfix(loss=losses.avg)


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ISCDataset(torch.utils.data.Dataset):
    def __init__(
            self,
            paths,
            transforms,
    ):
        self.paths = paths
        self.transforms = transforms

    def __len__(self):
        return len(self.paths)

    def __getitem__(self, i):
        image = Image.open(self.paths[i])
        image = self.transforms(image)
        return i, image


class NCropsTransform:
    """Take n random crops of one image as the query and key."""

    def __init__(self, aug_moderate, aug_hard, ncrops=2):
        self.aug_moderate = aug_moderate
        self.aug_hard = aug_hard
        self.ncrops = ncrops

    def __call__(self, x):
        return [self.aug_moderate(x)] + [self.aug_hard(x) for _ in range(self.ncrops - 1)]


class GaussianBlur(object):
    """Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""

    def __init__(self, sigma=[.1, 2.]):
        self.sigma = sigma

    def __call__(self, x):
        sigma = random.uniform(self.sigma[0], self.sigma[1])
        x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
        return x


class RandomOverlayText(BaseTransform):
    def __init__(
            self,
            opacity: float = 1.0,
            p: float = 1.0,
    ):
        super().__init__(p)
        self.opacity = opacity

        with open(Path(FONTS_DIR) / FONT_LIST_PATH) as f:
            font_list = [s.strip() for s in f.readlines()]
            blacklist = [
                'TypeMyMusic',
                'PainttheSky-Regular',
            ]
            self.font_list = [
                f for f in font_list
                if all(_ not in f for _ in blacklist)
            ]

        self.font_lens = []
        for ff in self.font_list:
            font_file = Path(MODULE_BASE_DIR) / ff.replace('.ttf', '.pkl')
            with open(font_file, 'rb') as f:
                self.font_lens.append(len(pickle.load(f)))

    def apply_transform(
            self, image: Image.Image, metadata: Optional[List[Dict[str, Any]]] = None, bboxes=None, bbox_format=None
    ) -> Image.Image:
        i = random.randrange(0, len(self.font_list))
        kwargs = dict(
            font_file=Path(MODULE_BASE_DIR) / self.font_list[i],
            font_size=random.uniform(0.1, 0.3),
            color=[random.randrange(0, 256) for _ in range(3)],
            x_pos=random.uniform(0.0, 0.5),
            metadata=metadata,
            opacity=self.opacity,
        )
        try:
            for j in range(random.randrange(1, 3)):
                if j == 0:
                    y_pos = random.uniform(0.0, 0.5)
                else:
                    y_pos += kwargs['font_size']
                image = overlay_text(
                    image,
                    text=[random.randrange(0, self.font_lens[i]) for _ in range(random.randrange(5, 10))],
                    y_pos=y_pos,
                    **kwargs,
                )
            return image
        except OSError:
            return image


class RandomOverlayImageAndResizedCrop(BaseTransform):
    def __init__(
            self,
            img_paths: List[Path],
            opacity_lower: float = 0.5,
            size_lower: float = 0.4,
            size_upper: float = 0.6,
            input_size: int = 224,
            moderate_scale_lower: float = 0.7,
            hard_scale_lower: float = 0.15,
            overlay_p: float = 0.05,
            p: float = 1.0,
    ):
        super().__init__(p)
        self.img_paths = img_paths
        self.opacity_lower = opacity_lower
        self.size_lower = size_lower
        self.size_upper = size_upper
        self.input_size = input_size
        self.moderate_scale_lower = moderate_scale_lower
        self.hard_scale_lower = hard_scale_lower
        self.overlay_p = overlay_p

    def apply_transform(
            self, image: Image.Image, metadata: Optional[List[Dict[str, Any]]] = None, bboxes=None, bbox_format=None
    ) -> Image.Image:

        if random.uniform(0.0, 1.0) < self.overlay_p:
            if random.uniform(0.0, 1.0) > 0.5:
                background = Image.open(random.choice(self.img_paths))
                overlay = image
            else:
                background = image
                overlay = Image.open(random.choice(self.img_paths))

            overlay_size = random.uniform(self.size_lower, self.size_upper)
            image = overlay_image(
                background,
                overlay=overlay,
                opacity=random.uniform(self.opacity_lower, 1.0),
                overlay_size=overlay_size,
                x_pos=random.uniform(0.0, 1.0 - overlay_size),
                y_pos=random.uniform(0.0, 1.0 - overlay_size),
                metadata=metadata,
            )
            return transforms.RandomResizedCrop(self.input_size, scale=(self.moderate_scale_lower, 1.))(image)
        else:
            return transforms.RandomResizedCrop(self.input_size, scale=(self.hard_scale_lower, 1.))(image)


class RandomEmojiOverlay(BaseTransform):
    def __init__(
            self,
            emoji_directory: str = SMILEY_EMOJI_DIR,
            opacity: float = 1.0,
            p: float = 1.0,
    ):
        super().__init__(p)
        self.emoji_directory = emoji_directory
        self.emoji_paths = pathmgr.ls(emoji_directory)
        self.opacity = opacity

    def apply_transform(
            self, image: Image.Image, metadata: Optional[List[Dict[str, Any]]] = None, bboxes=None, bbox_format=None
    ) -> Image.Image:
        emoji_path = random.choice(self.emoji_paths)
        return overlay_emoji(
            image,
            emoji_path=os.path.join(self.emoji_directory, emoji_path),
            opacity=self.opacity,
            emoji_size=random.uniform(0.1, 0.3),
            x_pos=random.uniform(0.0, 1.0),
            y_pos=random.uniform(0.0, 1.0),
            metadata=metadata,
        )


class RandomEdgeEnhance(BaseTransform):
    def __init__(
            self,
            mode=ImageFilter.EDGE_ENHANCE,
            p: float = 1.0,
    ):
        super().__init__(p)
        self.mode = mode

    def apply_transform(self, image: Image.Image, *args) -> Image.Image:
        return image.filter(self.mode)


class ShuffledAug:

    def __init__(self, aug_list):
        self.aug_list = aug_list

    def __call__(self, x):
        # without replacement
        shuffled_aug_list = random.sample(self.aug_list, len(self.aug_list))
        for op in shuffled_aug_list:
            x = op(x)
        return x


def convert2rgb(x):
    return x.convert('RGB')


def get_dataset(train_data_dir):
    input_size = 256
    ncrops = 2

    backbone = timm.create_model('tf_efficientnetv2_m_in21ft1k', features_only=True, pretrained=True)

    train_paths = list(Path(train_data_dir).glob('**/*.jpg'))

    aug_moderate = [
        transforms.RandomResizedCrop(input_size, scale=(0.7, 1.)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=backbone.default_cfg['mean'], std=backbone.default_cfg['std'])
    ]

    aug_list = [
        transforms.ColorJitter(0.7, 0.7, 0.7, 0.2),
        RandomPixelization(p=0.25),
        ShufflePixels(factor=0.1, p=0.25),
        OneOf([EncodingQuality(quality=q) for q in [10, 20, 30, 50]], p=0.25),
        transforms.RandomGrayscale(p=0.25),
        RandomBlur(p=0.25),
        transforms.RandomPerspective(p=0.25),
        transforms.RandomHorizontalFlip(p=0.5),
        transforms.RandomVerticalFlip(p=0.1),
        RandomOverlayText(p=0.25),
        RandomEmojiOverlay(p=0.25),
        OneOf([RandomEdgeEnhance(mode=ImageFilter.EDGE_ENHANCE),
               RandomEdgeEnhance(mode=ImageFilter.EDGE_ENHANCE_MORE)],
              p=0.25),
    ]
    aug_hard = [
        RandomRotation(p=0.25),
        RandomOverlayImageAndResizedCrop(
            train_paths, opacity_lower=0.6, size_lower=0.4, size_upper=0.6,
            input_size=input_size, moderate_scale_lower=0.7, hard_scale_lower=0.15, overlay_p=0.05, p=1.0,
        ),
        ShuffledAug(aug_list),
        convert2rgb,
        transforms.ToTensor(),
        transforms.RandomErasing(value='random', p=0.25),
        transforms.Normalize(mean=backbone.default_cfg['mean'], std=backbone.default_cfg['std']),
    ]

    train_dataset = ISCDataset(
        train_paths,
        NCropsTransform(
            transforms.Compose(aug_moderate),
            transforms.Compose(aug_hard),
            ncrops,
        ),
    )
    return train_dataset