import os
import json
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms

class RQADataset(Dataset):
    def __init__(self, data_dir, split='train', transform=None):
        """
        Initializes the dataset.

        Args:
            data_dir: Base directory of the dataset on the Hugging Face Hub.
            split: Split of the dataset ('train' or 'test').
            transform: Optional transform to be applied on a sample.
        """
        self.data_dir = data_dir
        self.split = split
        self.transform = transform or transforms.Compose([
            transforms.Resize((512, 512)),
            transforms.ToTensor()
        ])

        # Initialize lists to hold image and question data
        self.questions = []
        self.file_names = self._load_file_names()
        self._create_questions()
        print(f"Total Questions Loaded: {len(self.questions)}")

    def _load_file_names(self):
        """
        Loads the list of file names to be processed based on the split.

        Returns:
            A list of file names without extensions.
        """
        if self.split == 'test':
            # Load test file names from the list provided on Hugging Face
            filter_list_file = os.path.join(self.data_dir, 'test_filenames.txt')
            with open(filter_list_file, 'r') as f:
                file_names = [line.strip() for line in f]
            print(f"Loaded {len(file_names)} test files from {filter_list_file}")
        else:
            # For training, use all JSON files from all directories
            file_names = []
            for json_dir in ['jsons', 'jsons2', 'jsons3']:
                json_dir_path = os.path.join(self.data_dir, json_dir)
                json_files = [os.path.splitext(file)[0] for file in os.listdir(json_dir_path) if file.endswith('.json')]
                file_names.extend(json_files)
        return file_names

    def _create_questions(self):
        """
        Creates the list of questions from JSON files.
        """
        unused_count = 0
        for file_name in self.file_names:
            # Determine which folder contains the current JSON file
            if file_name in os.listdir(os.path.join(self.data_dir, 'jsons')):
                json_path = os.path.join(self.data_dir, 'jsons', f"{file_name}.json")
                img_dir = 'images'
            elif file_name in os.listdir(os.path.join(self.data_dir, 'jsons2')):
                json_path = os.path.join(self.data_dir, 'jsons2', f"{file_name}.json")
                img_dir = 'images2'
            else:
                json_path = os.path.join(self.data_dir, 'jsons3', f"{file_name}.json")
                img_dir = 'images3'

            # Load questions from the JSON file
            with open(json_path, 'r') as f:
                json_data = json.load(f)
                for item in json_data:
                    if 'PMC_ID' not in item or 'qa_id' not in item:
                        continue  # Ensure all necessary fields are present
                    item['image_path'] = os.path.join(self.data_dir, img_dir, f"{item['PMC_ID']}.jpg")
                    if os.path.exists(item['image_path']):
                        self.questions.append(item)
                    else:
                        unused_count += 1
        print(f"Total unused/used images: {unused_count} / {len(self.file_names) - unused_count}")

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

    def __getitem__(self, idx):
        """
        Loads a single data point.

        Args:
            idx: Index of the data point.

        Returns:
            A dictionary containing the image, question, and answer data.
        """
        question_block = self.questions[idx]
        image_path = question_block['image_path']
        image = Image.open(image_path).convert("RGB")

        # Apply transformation if available
        if self.transform:
            image = self.transform(image)

        return {
            'image': image,
            'question': question_block['question'],
            'answer': question_block['answer'],
            'qa_id': question_block['qa_id'],
            'PMC_ID': question_block['PMC_ID']
        }

    @staticmethod
    def custom_collate(batch):
        """
        Custom collate function to handle batch processing.

        Args:
            batch: A batch of data points.

        Returns:
            A dictionary containing the collated batch data.
        """
        images = [item['image'] for item in batch]
        questions = [item['question'] for item in batch]
        answers = [item['answer'] for item in batch]
        qa_ids = [item['qa_id'] for item in batch]
        pmc_ids = [item['PMC_ID'] for item in batch]

        return {
            'images': images,
            'questions': questions,
            'answers': answers,
            'qa_ids': qa_ids,
            'PMC_IDs': pmc_ids
        }

if __name__ == "__main__":
    # Initialize dataset for training
    dataset = RQADataset(data_dir='.', split='train')

    # Test loading a single item
    print(f"Number of samples in dataset: {len(dataset)}")
    sample = dataset[0]
    print("Sample data:", sample)

    # Initialize DataLoader
    dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate)

    # Test DataLoader
    for batch in dataloader:
        print("Batch data:", batch)
        break  # Load only one batch for testing