from queue import Queue
from threading import Thread

import h5py
import nltk
import torch
import torch.utils.data as data
import os
import numpy as np
import json
from torch.utils.data import DataLoader
from prefetch_generator import BackgroundGenerator
from transformers import BertTokenizer

class DataLoaderX(DataLoader):

    def __iter__(self):
        return BackgroundGenerator(super().__iter__())


class PrecompDataset(data.Dataset):
    """
    Load precomputed captions and image features
    Possible options: f30k_precomp, coco_precomp
    """
    def __init__(self, data_path, data_split, vocab):
        print('word txt encoder')
        self.vocab = vocab
        loc = data_path + '/'
        self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

        # Captions
        self.captions = []
        with open(loc+'%s_caps.txt' % data_split, 'rb') as f:
            for line in f:
                self.captions.append(line.strip().decode('utf-8'))

        self.data_split = data_split
        # if self.data_split == 'test':
        #     self.bbox = np.load(loc + '%s_ims_bbx.npy' % data_split)
        #     self.sizes = np.load(loc + '%s_ims_size.npy' % data_split, allow_pickle=True)

        # self.tags = []
        # with open(loc + '%s_tags_new.txt' % data_split, 'rb') as f:
        #     for line in f:
        #         self.tags.append(line.strip().decode('utf-8'))

        # Image features
        print('loading npy')
        self.images = np.load(loc+'%s_ims.npy' % data_split, mmap_mode = 'r')
        #self.images = np.load(loc + '%s_ims.npy' % data_split)
        print('done load npy')
        self.length = len(self.captions)
        # self.length = 10000
        # rkiros data has redundancy in images, we divide by 5, 10crop doesn't
        if self.images.shape[0] != self.length:
            self.im_div = 5
        else:
            self.im_div = 1
        # the development set for coco is large and so validation would be slow
        if data_split == 'dev':
            self.length = 5000


    def __getitem__(self, index):
        # handle the image redundancy
        img_id = int(index/self.im_div)
        image = torch.Tensor(self.images[img_id])
        caption = self.captions[index]
        vocab = self.vocab

        # caption = self.tokenizer.encode(caption)
        # target = torch.Tensor(caption)

        # Convert caption (string) to word ids.
        tokens = nltk.tokenize.word_tokenize(
            caption.encode('utf-8').decode('utf-8'))
        caption = []
        caption.append(vocab('<start>'))
        caption.extend([vocab(str(token).lower()) for token in tokens])
        caption.append(vocab('<end>'))
        # assert(len(caption) - 2== len(new_tags))
        target = torch.Tensor(caption)
        # new_tags = torch.Tensor(new_tags)

        return image, target, index, img_id

    def __len__(self):
        return self.length


def collate_fn(data):
    """Build mini-batch tensors from a list of (image, caption) tuples.
    Args:
        data: list of (image, caption) tuple.
            - 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
    data.sort(key=lambda x: len(x[1]), reverse=True)

    images, captions, ids, img_ids = zip(*data)

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

    # Merget captions (convert tuple of 1D tensor to 2D tensor)
    lengths = torch.LongTensor([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, ids


def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100,
                       shuffle=True, num_workers=0):
    """Returns torch.utils.data.DataLoader for custom coco dataset."""

    dset = PrecompDataset(data_path, data_split, vocab)
    # train_sampler = torch.utils.data.distributed.DistributedSampler(dset)
    # if data_split == 'train':
    #     data_loader = DataLoader(dataset=dset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True, collate_fn=collate_fn, sampler=train_sampler)
    # else:
    print(num_workers)
    data_loader = DataLoaderX(dataset=dset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=True, collate_fn=collate_fn)

    return data_loader


def get_loaders(data_name, vocab, batch_size, workers, opt):
    dpath = os.path.join(opt.data_path, data_name)
    train_loader = get_precomp_loader(dpath, 'train', vocab, opt,
                                      batch_size, True, workers)
    val_loader = get_precomp_loader(dpath, 'dev', vocab, opt,
                                    batch_size, False, workers)
    return train_loader, val_loader


def get_test_loader2(split_name, data_name, vocab, batch_size,
                    workers, opt):
    dpath = os.path.join(opt.data_path, data_name)
    test_loader = get_precomp_loader(dpath, split_name, vocab, opt,
                                     batch_size, False, workers)
    return test_loader