""" Dataset Factory

"""
import os

'''
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, QMNIST, KMNIST, FashionMNIST, ImageNet, ImageFolder
try:
    from torchvision.datasets import Places365
    has_places365 = True
except ImportError:
    has_places365 = False
try:
    from torchvision.datasets import INaturalist
    has_inaturalist = True
except ImportError:
    has_inaturalist = False

from .dataset import IterableImageDataset, ImageDataset
'''

# import torch
from .gate_dataset import GATEDateset
from .cnf_dataset import CNFDataset
from .ckt_dataset import CircuitSATDataset

_dataset_factory = {
  'gate': GATEDateset,
  'cnf': CNFDataset,
  'ckt': CircuitSATDataset
}

'''
_TORCH_BASIC_DS = dict(
    cifar10=CIFAR10,
    cifar100=CIFAR100,
    mnist=MNIST,
    qmist=QMNIST,
    kmnist=KMNIST,
    fashion_mnist=FashionMNIST,
)
_TRAIN_SYNONYM = dict(train=None, training=None)
_EVAL_SYNONYM = dict(val=None, valid=None, validation=None, eval=None, evaluation=None)


def _search_split(root, split):
    # look for sub-folder with name of split in root and use that if it exists
    split_name = split.split('[')[0]
    try_root = os.path.join(root, split_name)
    if os.path.exists(try_root):
        return try_root

    def _try(syn):
        for s in syn:
            try_root = os.path.join(root, s)
            if os.path.exists(try_root):
                return try_root
        return root
    if split_name in _TRAIN_SYNONYM:
        root = _try(_TRAIN_SYNONYM)
    elif split_name in _EVAL_SYNONYM:
        root = _try(_EVAL_SYNONYM)
    return root
'''

'''
def create_dataset(
        name,
        root,
        split='validation',
        search_split=True,
        class_map=None,
        load_bytes=False,
        is_training=False,
        download=False,
        batch_size=None,
        repeats=0,
        **kwargs
):
    """ Dataset factory method

    In parenthesis after each arg are the type of dataset supported for each arg, one of:
      * folder - default, timm folder (or tar) based ImageDataset
      * torch - torchvision based datasets
      * TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
      * all - any of the above

    Args:
        name: dataset name, empty is okay for folder based datasets
        root: root folder of dataset (all)
        split: dataset split (all)
        search_split: search for split specific child fold from root so one can specify
            `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (folder, torch/folder)
        class_map: specify class -> index mapping via text file or dict (folder)
        load_bytes: load data, return images as undecoded bytes (folder)
        download: download dataset if not present and supported (TFDS, torch)
        is_training: create dataset in train mode, this is different from the split.
            For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS)
        batch_size: batch size hint for (TFDS)
        repeats: dataset repeats per iteration i.e. epoch (TFDS)
        **kwargs: other args to pass to dataset

    Returns:
        Dataset object
    """
    name = name.lower()
    if name.startswith('torch/'):
        name = name.split('/', 2)[-1]
        torch_kwargs = dict(root=root, download=download, **kwargs)
        if name in _TORCH_BASIC_DS:
            ds_class = _TORCH_BASIC_DS[name]
            use_train = split in _TRAIN_SYNONYM
            ds = ds_class(train=use_train, **torch_kwargs)
        elif name == 'inaturalist' or name == 'inat':
            assert has_inaturalist, 'Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalist'
            target_type = 'full'
            split_split = split.split('/')
            if len(split_split) > 1:
                target_type = split_split[0].split('_')
                if len(target_type) == 1:
                    target_type = target_type[0]
                split = split_split[-1]
            if split in _TRAIN_SYNONYM:
                split = '2021_train'
            elif split in _EVAL_SYNONYM:
                split = '2021_valid'
            ds = INaturalist(version=split, target_type=target_type, **torch_kwargs)
        elif name == 'places365':
            assert has_places365, 'Please update to a newer PyTorch and torchvision for Places365 dataset.'
            if split in _TRAIN_SYNONYM:
                split = 'train-standard'
            elif split in _EVAL_SYNONYM:
                split = 'val'
            ds = Places365(split=split, **torch_kwargs)
        elif name == 'imagenet':
            if split in _EVAL_SYNONYM:
                split = 'val'
            ds = ImageNet(split=split, **torch_kwargs)
        elif name == 'image_folder' or name == 'folder':
            # in case torchvision ImageFolder is preferred over timm ImageDataset for some reason
            if search_split and os.path.isdir(root):
                # look for split specific sub-folder in root
                root = _search_split(root, split)
            ds = ImageFolder(root, **kwargs)
        else:
            assert False, f"Unknown torchvision dataset {name}"
    elif name.startswith('tfds/'):
        ds = IterableImageDataset(
            root, parser=name, split=split, is_training=is_training,
            download=download, batch_size=batch_size, repeats=repeats, **kwargs)
    else:
        # FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
        if search_split and os.path.isdir(root):
            # look for split specific sub-folder in root
            root = _search_split(root, split)
        ds = ImageDataset(root, parser=name, class_map=class_map, load_bytes=load_bytes, **kwargs)
    return ds
'''


def create_dataset(
        name,
        root,
        # split='validation',
        split=0.9,
        **kwargs
        # search_split=True,
        # # class_map=None,
        # load_bytes=False,
        # is_training=False,
        # # download=False,
        # batch_size=None,
        # repeats=0,
        # **kwargs
):
    """ Dataset factory method

    In parenthesis after each arg are the type of dataset supported for each arg, one of:
      * folder - default, timm folder (or tar) based ImageDataset
      * torch - torchvision based datasets
      * TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
      * all - any of the above

    Args:
        name: dataset name, empty is okay for folder based datasets
        root: root folder of dataset (all)
        split: dataset split (all)
        search_split: search for split specific child fold from root so one can specify
            `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (folder, torch/folder)
        class_map: specify class -> index mapping via text file or dict (folder)
        load_bytes: load data, return images as undecoded bytes (folder)
        download: download dataset if not present and supported (TFDS, torch)
        is_training: create dataset in train mode, this is different from the split.
            For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS)
        batch_size: batch size hint for (TFDS)
        repeats: dataset repeats per iteration i.e. epoch (TFDS)
        **kwargs: other args to pass to dataset

    Returns:
        Dataset object
    """
    ds = _dataset_factory[name](root, **kwargs)
    ds = ds.shuffle()
    data_len = len(ds)
    training_cutoff = int(data_len * split)
    return ds[:training_cutoff], ds[training_cutoff:]

    '''
    name = name.lower()
    if name.startswith('torch/'):
        name = name.split('/', 2)[-1]
        torch_kwargs = dict(root=root, download=download, **kwargs)
        if name in _TORCH_BASIC_DS:
            ds_class = _TORCH_BASIC_DS[name]
            use_train = split in _TRAIN_SYNONYM
            ds = ds_class(train=use_train, **torch_kwargs)
        elif name == 'inaturalist' or name == 'inat':
            assert has_inaturalist, 'Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalist'
            target_type = 'full'
            split_split = split.split('/')
            if len(split_split) > 1:
                target_type = split_split[0].split('_')
                if len(target_type) == 1:
                    target_type = target_type[0]
                split = split_split[-1]
            if split in _TRAIN_SYNONYM:
                split = '2021_train'
            elif split in _EVAL_SYNONYM:
                split = '2021_valid'
            ds = INaturalist(version=split, target_type=target_type, **torch_kwargs)
        elif name == 'places365':
            assert has_places365, 'Please update to a newer PyTorch and torchvision for Places365 dataset.'
            if split in _TRAIN_SYNONYM:
                split = 'train-standard'
            elif split in _EVAL_SYNONYM:
                split = 'val'
            ds = Places365(split=split, **torch_kwargs)
        elif name == 'imagenet':
            if split in _EVAL_SYNONYM:
                split = 'val'
            ds = ImageNet(split=split, **torch_kwargs)
        elif name == 'image_folder' or name == 'folder':
            # in case torchvision ImageFolder is preferred over timm ImageDataset for some reason
            if search_split and os.path.isdir(root):
                # look for split specific sub-folder in root
                root = _search_split(root, split)
            ds = ImageFolder(root, **kwargs)
        else:
            assert False, f"Unknown torchvision dataset {name}"
    elif name.startswith('tfds/'):
        ds = IterableImageDataset(
            root, parser=name, split=split, is_training=is_training,
            download=download, batch_size=batch_size, repeats=repeats, **kwargs)
    else:
        # FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
        if search_split and os.path.isdir(root):
            # look for split specific sub-folder in root
            root = _search_split(root, split)
        ds = ImageDataset(root, parser=name, class_map=class_map, load_bytes=load_bytes, **kwargs)
    return ds
    '''