import torch.utils.data as data
from detectron2.utils.serialize import PicklableWrapper

__all__ = ["MapPairDataset", "AspectRatioGroupedPairDataset"]


class MapPairDataset(data.Dataset):
    """
    Map a function over the elements in a dataset.

    Args:
        dataset: a dataset where map function is applied.
        map_func: a callable which maps the element in dataset. map_func is
            responsible for error handling, when error happens, it needs to
            return None so the MapDataset will randomly use other
            elements from the dataset.
    """

    def __init__(self, dataset, map_func):
        self._dataset = dataset
        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work

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

    def __getitem__(self, pair_idx):
        return self._map_func([self._dataset[idx] for idx in pair_idx])


class AspectRatioGroupedPairDataset(data.IterableDataset):
    """
    Batch data that have similar aspect ratio together.
    In this implementation, images whose aspect ratio < (or >) 1 will
    be batched together.
    This improves training speed because the images then need less padding
    to form a batch.

    It assumes the underlying dataset produces dicts with "width" and "height" keys.
    It will then produce a list of original dicts with length = batch_size,
    all with similar aspect ratios.
    """

    def __init__(self, dataset, batch_size):
        """
        Args:
            dataset: an iterable. Each element must be a dict with keys
                "width" and "height", which will be used to batch data.
            batch_size (int):
        """
        self._dataset = dataset
        self.batch_size = batch_size
        self._buckets = [[] for _ in range(2)]
        # Hard-coded two aspect ratio groups: w > h and w < h.
        # Can add support for more aspect ratio groups, but doesn't seem useful

    def __iter__(self):
        for d in self._dataset:
            # suppose pair data have the same shape
            w, h = d[0]["width"], d[0]["height"]
            bucket_id = 0 if w > h else 1
            bucket = self._buckets[bucket_id]
            bucket.append(d)
            if len(bucket) == self.batch_size:
                yield [pair_data[i] for i in range(len(d)) for pair_data in bucket]
                del bucket[:]
