# Copyright (c) 2018-2019, NVIDIA CORPORATION
# Copyright (c) 2017-      Facebook, Inc
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import os
import torch
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from PIL import Image
from functools import partial
from torchvision.transforms.functional import InterpolationMode

from image_classification.autoaugment import AutoaugmentImageNetPolicy

DATA_BACKEND_CHOICES = ["pytorch", "synthetic"]
try:
    from nvidia.dali.plugin.pytorch import DALIClassificationIterator
    from nvidia.dali.pipeline import Pipeline
    import nvidia.dali.ops as ops
    import nvidia.dali.types as types

    DATA_BACKEND_CHOICES.append("dali-gpu")
    DATA_BACKEND_CHOICES.append("dali-cpu")
except ImportError:
    print(
        "Please install DALI from https://www.github.com/NVIDIA/DALI to run this example."
    )


def load_jpeg_from_file(path, cuda=True):
    img_transforms = transforms.Compose(
        [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]
    )

    img = img_transforms(Image.open(path))
    with torch.no_grad():
        # mean and std are not multiplied by 255 as they are in training script
        # torch dataloader reads data into bytes whereas loading directly
        # through PIL creates a tensor with floats in [0,1] range
        mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
        std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)

        if cuda:
            mean = mean.cuda()
            std = std.cuda()
            img = img.cuda()
        img = img.float()

        input = img.unsqueeze(0).sub_(mean).div_(std)

    return input


class HybridTrainPipe(Pipeline):
    def __init__(
        self,
        batch_size,
        num_threads,
        device_id,
        data_dir,
        interpolation,
        crop,
        dali_cpu=False,
    ):
        super(HybridTrainPipe, self).__init__(
            batch_size, num_threads, device_id, seed=12 + device_id
        )
        interpolation = {
            "bicubic": types.INTERP_CUBIC,
            "bilinear": types.INTERP_LINEAR,
            "triangular": types.INTERP_TRIANGULAR,
        }[interpolation]
        if torch.distributed.is_initialized():
            rank = torch.distributed.get_rank()
            world_size = torch.distributed.get_world_size()
        else:
            rank = 0
            world_size = 1

        self.input = ops.FileReader(
            file_root=data_dir,
            shard_id=rank,
            num_shards=world_size,
            random_shuffle=True,
            pad_last_batch=True,
        )

        if dali_cpu:
            dali_device = "cpu"
            self.decode = ops.ImageDecoder(device=dali_device, output_type=types.RGB)
        else:
            dali_device = "gpu"
            # This padding sets the size of the internal nvJPEG buffers to be able to handle all images from full-sized ImageNet
            # without additional reallocations
            self.decode = ops.ImageDecoder(
                device="mixed",
                output_type=types.RGB,
                device_memory_padding=211025920,
                host_memory_padding=140544512,
            )

        self.res = ops.RandomResizedCrop(
            device=dali_device,
            size=[crop, crop],
            interp_type=interpolation,
            random_aspect_ratio=[0.75, 4.0 / 3.0],
            random_area=[0.08, 1.0],
            num_attempts=100,
            antialias=False,
        )

        self.cmnp = ops.CropMirrorNormalize(
            device="gpu",
            dtype=types.FLOAT,
            output_layout=types.NCHW,
            crop=(crop, crop),
            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
            std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
        )
        self.coin = ops.CoinFlip(probability=0.5)

    def define_graph(self):
        rng = self.coin()
        self.jpegs, self.labels = self.input(name="Reader")
        images = self.decode(self.jpegs)
        images = self.res(images)
        output = self.cmnp(images.gpu(), mirror=rng)
        return [output, self.labels]


class HybridValPipe(Pipeline):
    def __init__(
        self, batch_size, num_threads, device_id, data_dir, interpolation, crop, size
    ):
        super(HybridValPipe, self).__init__(
            batch_size, num_threads, device_id, seed=12 + device_id
        )
        interpolation = {
            "bicubic": types.INTERP_CUBIC,
            "bilinear": types.INTERP_LINEAR,
            "triangular": types.INTERP_TRIANGULAR,
        }[interpolation]
        if torch.distributed.is_initialized():
            rank = torch.distributed.get_rank()
            world_size = torch.distributed.get_world_size()
        else:
            rank = 0
            world_size = 1

        self.input = ops.FileReader(
            file_root=data_dir,
            shard_id=rank,
            num_shards=world_size,
            random_shuffle=False,
            pad_last_batch=True,
        )

        self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB)
        self.res = ops.Resize(
            device="gpu",
            resize_shorter=size,
            interp_type=interpolation,
            antialias=False,
        )
        self.cmnp = ops.CropMirrorNormalize(
            device="gpu",
            dtype=types.FLOAT,
            output_layout=types.NCHW,
            crop=(crop, crop),
            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
            std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
        )

    def define_graph(self):
        self.jpegs, self.labels = self.input(name="Reader")
        images = self.decode(self.jpegs)
        images = self.res(images)
        output = self.cmnp(images)
        return [output, self.labels]


class DALIWrapper(object):
    def gen_wrapper(dalipipeline, num_classes, one_hot, memory_format):
        for data in dalipipeline:
            input = data[0]["data"].contiguous(memory_format=memory_format)
            target = torch.reshape(data[0]["label"], [-1]).cuda().long()
            if one_hot:
                target = expand(num_classes, torch.float, target)
            yield input, target
        dalipipeline.reset()

    def __init__(self, dalipipeline, num_classes, one_hot, memory_format):
        self.dalipipeline = dalipipeline
        self.num_classes = num_classes
        self.one_hot = one_hot
        self.memory_format = memory_format

    def __iter__(self):
        return DALIWrapper.gen_wrapper(
            self.dalipipeline, self.num_classes, self.one_hot, self.memory_format
        )


def get_dali_train_loader(dali_cpu=False):
    def gdtl(
        data_path,
        image_size,
        batch_size,
        num_classes,
        one_hot,
        interpolation="bilinear",
        augmentation=None,
        start_epoch=0,
        workers=5,
        _worker_init_fn=None,
        memory_format=torch.contiguous_format,
        **kwargs,
    ):
        if torch.distributed.is_initialized():
            rank = torch.distributed.get_rank()
            world_size = torch.distributed.get_world_size()
        else:
            rank = 0
            world_size = 1

        traindir = os.path.join(data_path, "train")
        if augmentation is not None:
            raise NotImplementedError(
                f"Augmentation {augmentation} for dali loader is not supported"
            )

        pipe = HybridTrainPipe(
            batch_size=batch_size,
            num_threads=workers,
            device_id=rank % torch.cuda.device_count(),
            data_dir=traindir,
            interpolation=interpolation,
            crop=image_size,
            dali_cpu=dali_cpu,
        )

        pipe.build()
        train_loader = DALIClassificationIterator(
            pipe, reader_name="Reader", fill_last_batch=False
        )

        return (
            DALIWrapper(train_loader, num_classes, one_hot, memory_format),
            int(pipe.epoch_size("Reader") / (world_size * batch_size)),
        )

    return gdtl


def get_dali_val_loader():
    def gdvl(
        data_path,
        image_size,
        batch_size,
        num_classes,
        one_hot,
        interpolation="bilinear",
        crop_padding=32,
        workers=5,
        _worker_init_fn=None,
        memory_format=torch.contiguous_format,
        **kwargs,
    ):
        if torch.distributed.is_initialized():
            rank = torch.distributed.get_rank()
            world_size = torch.distributed.get_world_size()
        else:
            rank = 0
            world_size = 1

        valdir = os.path.join(data_path, "val")

        pipe = HybridValPipe(
            batch_size=batch_size,
            num_threads=workers,
            device_id=rank % torch.cuda.device_count(),
            data_dir=valdir,
            interpolation=interpolation,
            crop=image_size,
            size=image_size + crop_padding,
        )

        pipe.build()
        val_loader = DALIClassificationIterator(
            pipe, reader_name="Reader", fill_last_batch=False
        )

        return (
            DALIWrapper(val_loader, num_classes, one_hot, memory_format),
            int(pipe.epoch_size("Reader") / (world_size * batch_size)),
        )

    return gdvl


def fast_collate(memory_format, batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8).contiguous(
        memory_format=memory_format
    )
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)

        tensor[i] += torch.from_numpy(nump_array.copy())

    return tensor, targets


def expand(num_classes, dtype, tensor):
    e = torch.zeros(
        tensor.size(0), num_classes, dtype=dtype, device=torch.device("cuda")
    )
    e = e.scatter(1, tensor.unsqueeze(1), 1.0)
    return e


class PrefetchedWrapper(object):
    def prefetched_loader(loader, num_classes, one_hot):
        mean = (
            torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255])
            .cuda()
            .view(1, 3, 1, 1)
        )
        std = (
            torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255])
            .cuda()
            .view(1, 3, 1, 1)
        )

        stream = torch.cuda.Stream()
        first = True

        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                if one_hot:
                    next_target = expand(num_classes, torch.float, next_target)

                next_input = next_input.sub_(mean).div_(std)

            if not first:
                yield input, target
            else:
                first = False

            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target

        yield input, target

    def __init__(self, dataloader, start_epoch, num_classes, one_hot):
        self.dataloader = dataloader
        self.epoch = start_epoch
        self.one_hot = one_hot
        self.num_classes = num_classes

    def __iter__(self):
        if self.dataloader.sampler is not None and isinstance(
            self.dataloader.sampler, torch.utils.data.distributed.DistributedSampler
        ):

            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(
            self.dataloader, self.num_classes, self.one_hot
        )

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


def get_pytorch_train_loader(
    data_path,
    image_size,
    batch_size,
    num_classes,
    one_hot,
    interpolation="bilinear",
    augmentation=None,
    start_epoch=0,
    workers=5,
    _worker_init_fn=None,
    prefetch_factor=2,
    memory_format=torch.contiguous_format,
):
    interpolation = {
        "bicubic": InterpolationMode.BICUBIC,
        "bilinear": InterpolationMode.BILINEAR,
    }[interpolation]
    traindir = os.path.join(data_path, "train")
    transforms_list = [
        transforms.RandomResizedCrop(image_size, interpolation=interpolation),
        transforms.RandomHorizontalFlip(),
    ]
    if augmentation == "autoaugment":
        transforms_list.append(AutoaugmentImageNetPolicy())
    train_dataset = datasets.ImageFolder(traindir, transforms.Compose(transforms_list))

    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset, shuffle=True
        )
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        sampler=train_sampler,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True,
        collate_fn=partial(fast_collate, memory_format),
        drop_last=True,
        persistent_workers=True,
        prefetch_factor=prefetch_factor,
    )

    return (
        PrefetchedWrapper(train_loader, start_epoch, num_classes, one_hot),
        len(train_loader),
    )


def get_pytorch_val_loader(
    data_path,
    image_size,
    batch_size,
    num_classes,
    one_hot,
    interpolation="bilinear",
    workers=5,
    _worker_init_fn=None,
    crop_padding=32,
    memory_format=torch.contiguous_format,
    prefetch_factor=2,
):
    interpolation = {
        "bicubic": InterpolationMode.BICUBIC,
        "bilinear": InterpolationMode.BILINEAR,
    }[interpolation]
    valdir = os.path.join(data_path, "val")
    val_dataset = datasets.ImageFolder(
        valdir,
        transforms.Compose(
            [
                transforms.Resize(
                    image_size + crop_padding, interpolation=interpolation
                ),
                transforms.CenterCrop(image_size),
            ]
        ),
    )

    if torch.distributed.is_initialized():
        val_sampler = torch.utils.data.distributed.DistributedSampler(
            val_dataset, shuffle=False
        )
    else:
        val_sampler = None

    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        sampler=val_sampler,
        batch_size=batch_size,
        shuffle=(val_sampler is None),
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True,
        collate_fn=partial(fast_collate, memory_format),
        drop_last=False,
        persistent_workers=True,
        prefetch_factor=prefetch_factor,
    )

    return PrefetchedWrapper(val_loader, 0, num_classes, one_hot), len(val_loader)


class SynteticDataLoader(object):
    def __init__(
        self,
        batch_size,
        num_classes,
        num_channels,
        height,
        width,
        one_hot,
        memory_format=torch.contiguous_format,
    ):
        input_data = (
            torch.randn(batch_size, num_channels, height, width)
            .contiguous(memory_format=memory_format)
            .cuda()
            .normal_(0, 1.0)
        )
        if one_hot:
            input_target = torch.empty(batch_size, num_classes).cuda()
            input_target[:, 0] = 1.0
        else:
            input_target = torch.randint(0, num_classes, (batch_size,))
        input_target = input_target.cuda()

        self.input_data = input_data
        self.input_target = input_target

    def __iter__(self):
        while True:
            yield self.input_data, self.input_target


def get_synthetic_loader(
    data_path,
    image_size,
    batch_size,
    num_classes,
    one_hot,
    interpolation=None,
    augmentation=None,
    start_epoch=0,
    workers=None,
    _worker_init_fn=None,
    memory_format=torch.contiguous_format,
    **kwargs,
):
    return (
        SynteticDataLoader(
            batch_size,
            num_classes,
            3,
            image_size,
            image_size,
            one_hot,
            memory_format=memory_format,
        ),
        -1,
    )
