import os
import json
import torch
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from glob import glob
from data.utils import pre_caption


class facecaption_train(Dataset):
    def __init__(self, transform, image_root, ann_root, max_words=65, prompt=''):        
        '''
        image_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        '''
        all_json = sorted(glob(os.path.join(ann_root, '*.json')))
        self.annotation = []
        for json_path in all_json[0:1]:
        # for json_path in all_json[:-1]:
            with open(json_path, 'r') as json_file:
                data = json.load(json_file)
                self.annotation.extend(data)

        self.transform = transform
        self.image_root = image_root
        self.max_words = max_words
        self.prompt = prompt
        
        self.img_ids = {}
        n = 0
        for ann in self.annotation:
            img_id = ann['image_id']#[7:]
            if img_id not in self.img_ids.keys():
                self.img_ids[img_id] = n
                n += 1    
        
    def __len__(self):
        return len(self.annotation)
    
    def __getitem__(self, index):    
        
        ann = self.annotation[index]
        image_path = os.path.join(self.image_root, ann['image'])  
        image = Image.open(image_path).convert('RGB') 
        image = self.transform(image)
        
        
        caption = self.prompt + pre_caption(*ann['caption'], self.max_words)
        image_id = self.img_ids[ann['image_id']]
        return image, caption, image_id
    

class facecaption_test(Dataset):
    def __init__(self, transform, image_root, ann_root, max_words=65):        
        '''
        image_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        '''
        all_json = sorted(glob(os.path.join(ann_root, '*.json')))
        self.annotation = []
        for json_path in all_json[-1:]:
            with open(json_path, 'r') as json_file:
                data = json.load(json_file)
                self.annotation.extend(data)
        self.annotation = self.annotation[:5000]

        self.transform = transform
        self.image_root = image_root
        
        self.text = []
        self.image = []
        self.txt2img = {}
        self.img2txt = {}

        txt_id = 0
        for img_id, ann in enumerate(self.annotation):
            self.image.append(ann['image'])  
            self.img2txt[img_id] = []
            for i, caption in enumerate(ann['caption']):    
                self.text.append(pre_caption(caption, max_words))
                self.img2txt[img_id].append(txt_id)
                self.txt2img[txt_id] = img_id
                txt_id += 1
        
    def __len__(self):
        return len(self.annotation)
    
    def __getitem__(self, index):    
        
        ann = self.annotation[index]
        
        image_path = os.path.join(self.image_root, ann['image'])    
        image = Image.open(image_path).convert('RGB') 
        image = self.transform(image)
        return image, index