"""
__init__.py
Enables dynamic loading of datasets, depending on an argument.
"""
import importlib
import torch.utils.data
from data.base_dataset import BaseDataset


def find_dataset_using_name(dataset_name):
    """Import the module "data/[dataset_name]_dataset.py".

    In the file, the class called DatasetNameDataset() will
    be instantiated. It has to be a subclass of BaseDataset,
    and it is case-insensitive.
    """
    dataset_filename = "data." + dataset_name + "_dataset"
    datasetlib = importlib.import_module(dataset_filename)

    dataset = None
    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
    for name, cls in datasetlib.__dict__.items():
        if name.lower() == target_dataset_name.lower() \
           and issubclass(cls, BaseDataset):
            dataset = cls

    if dataset is None:
        raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))

    return dataset


def create_dataset(opt):
    """Create a dataset given the option.

    This function wraps the class CustomDatasetDataLoader.
        This is the main interface between this package and 'train.py'/'test.py'

    Example:
        >>> from data import create_dataset
        >>> dataset = create_dataset(opt)
    """
    data_loader = CustomDatasetDataLoader(opt)
    dataset = data_loader.load_data()
    return dataset

def get_dataset_options(dataset_name):
    dataset_filename = "data." + dataset_name + "_dataset"
    datalib = importlib.import_module(dataset_filename)
    for name, cls in datalib.__dict__.items():
        if name.lower() == 'datasetoptions':
            return cls
    return None

class CustomDatasetDataLoader():
    """Wrapper class of Dataset class that performs multi-threaded data loading"""

    def __init__(self, opt):
        """Initialize this class

        Step 1: create a dataset instance given the name [dataset_mode]
        Step 2: create a multi-threaded data loader.
        """
        self.opt = opt
        dataset_class = find_dataset_using_name(opt.dataset_mode)
        self.dataset = dataset_class(opt)
        self.dataloader = torch.utils.data.DataLoader(
            self.dataset,
            batch_size=opt.batch_size,
            shuffle=not opt.serial_batches,
            num_workers=int(opt.num_threads))

    def load_data(self):
        return self

    def __len__(self):
        """Return the number of data in the dataset"""
        return min(len(self.dataset), self.opt.max_dataset_size)

    def __iter__(self):
        """Return a batch of data"""
        for i, data in enumerate(self.dataloader):
            if i * self.opt.batch_size >= self.opt.max_dataset_size:
                break
            yield data
