import os

import torch
from PIL import Image
from torch.utils import data


class CocoDataset(data.Dataset):
    """COCO Custom Dataset compatible with torch.utils.data.DataLoader."""

    def __init__(self, root, json, vocab, transform=None):
        """Set the path for images, captions and vocabulary wrapper.

        Args:
            root: image directory.
            json: coco annotation file path.
            vocab: vocabulary wrapper.
            transform: image transformer.

        """
        self.root = root

        from pycocotools.coco import COCO

        self.coco = COCO(json)
        self.ids = list(self.coco.anns.keys())
        self.vocab = vocab
        self.transform = transform

    def __getitem__(self, index):
        """Returns one data pair (image and caption)."""
        coco = self.coco
        vocab = self.vocab
        ann_id = self.ids[index]
        caption = coco.anns[ann_id]["caption"]
        img_id = coco.anns[ann_id]["image_id"]
        path = coco.loadImgs(img_id)[0]["file_name"]

        image = Image.open(os.path.join(self.root, path)).convert("RGB")
        if self.transform is not None:
            image = self.transform(image)

        # Convert caption (string) to word ids.

        import nltk

        tokens = nltk.tokenize.word_tokenize(str(caption).lower())
        caption = []
        caption.append(vocab("<start>"))
        caption.extend([vocab(token) for token in tokens])
        caption.append(vocab("<end>"))
        target = torch.Tensor(caption)
        return image, target

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


def collate_fn(data):
    """Creates mini-batch tensors from the list of tuples (image, caption).

    We should build custom collate_fn rather than using default collate_fn,
    because merging caption (including padding) is not supported in default.

    Args:
        data: list of tuple (image, caption).
            - image: torch tensor of shape (3, 256, 256).
            - caption: torch tensor of shape (?); variable length.

    Returns:
        images: torch tensor of shape (batch_size, 3, 256, 256).
        targets: torch tensor of shape (batch_size, padded_length).
        lengths: list; valid length for each padded caption.

    """
    # Sort a data list by caption length (descending order).
    data.sort(key=lambda x: len(x[1]), reverse=True)
    images, captions = zip(*data)

    # Merge images (from tuple of 3D tensor to 4D tensor).
    images = torch.stack(images, 0)

    # Merge captions (from tuple of 1D tensor to 2D tensor).
    lengths = [len(cap) for cap in captions]
    targets = torch.zeros(len(captions), max(lengths)).long()
    for i, cap in enumerate(captions):
        end = lengths[i]
        targets[i, :end] = cap[:end]
    return images, targets, lengths


def get_loader(root, json, vocab, transform, batch_size, shuffle, num_workers):
    """Returns torch.utils.data.DataLoader for custom coco dataset."""
    # COCO caption dataset
    coco = CocoDataset(root=root, json=json, vocab=vocab, transform=transform)

    # Data loader for COCO dataset
    # This will return (images, captions, lengths) for each iteration.
    # images: a tensor of shape (batch_size, 3, 224, 224).
    # captions: a tensor of shape (batch_size, padded_length).
    # lengths: a list indicating valid length for each caption. length is (batch_size).
    data_loader = torch.utils.data.DataLoader(
        dataset=coco,
        batch_size=batch_size,
        shuffle=shuffle,
        num_workers=num_workers,
        collate_fn=collate_fn,
    )
    return data_loader
