import json
import os
import random
import csv
from typing import List, Dict, Set, Tuple
from PIL import Image
from tqdm import tqdm
from collections import defaultdict


def build_image_cache(modality_to_dir: Dict[str, str], data: List[Dict], base_dir: str = '.') -> Tuple[Set[str], Dict[Tuple[str, str], List[str]]]:
    """
    Build a set of used filenames and a cache of available images per (modality, category).
    """
    print("Building image cache...")
    used_filenames = {os.path.basename(item['image_path']) for item in data}
    available_images_cache = {}
    for modality, dir_path in modality_to_dir.items():
        full_modality_dir = os.path.join(base_dir, dir_path)
        if not os.path.exists(full_modality_dir):
            continue
        for category in os.listdir(full_modality_dir):
            category_dir = os.path.join(full_modality_dir, category)
            if not os.path.isdir(category_dir):
                continue
            all_images = [f for f in os.listdir(category_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]
            available_images = [img for img in all_images if img not in used_filenames]
            # Convert paths to Linux style and ensure they start with RadImageNet
            available_images_cache[(modality, category)] = [
                os.path.join('RadImageNet', 'radiology_ai', 
                           os.path.basename(dir_path),  # CT, MR, or US
                           category, img).replace('\\', '/') 
                for img in available_images
            ]
    print(f"Image cache built. {len(available_images_cache)} (modality, category) pairs cached.")
    return used_filenames, available_images_cache


def get_random_image_path_cached(modality_type: str, original_image_path: str, available_images_cache: Dict[Tuple[str, str], List[str]]) -> str:
    """
    Get a random image path using the pre-built cache.
    """
    # Map modality type to directory (must match main)
    modality_to_dir = {
        "CT(Computed Tomography)": "RadImageNet/radiology_ai/CT",
        "MR (Mag-netic Resonance Imaging)": "RadImageNet/radiology_ai/MR",
        "ultrasound": "RadImageNet/radiology_ai/US"
    }
    
    if modality_type not in modality_to_dir:
        raise ValueError(f"Unknown modality type: {modality_type}")
    
    category = original_image_path.split("/")[-2]
    key = (modality_type, category)
    available_images = available_images_cache.get(key, [])
    
    if not available_images:
        raise ValueError(f"No unused images found for modality {modality_type} and category {category}")
    
    chosen_file = random.choice(available_images)
    # Remove chosen file from cache to avoid reuse
    available_images_cache[key].remove(chosen_file)
    return chosen_file


def extract_unique_questions(json_data: List[Dict], available_images_cache: Dict[Tuple[str, str], List[str]]) -> Dict[str, Dict]:
    """
    Extract unique questions from the JSON data where questions with different answers are considered different.
    Uses the cached available images for efficiency.
    """
    print("Extracting unique questions...")
    unique_questions = {}
    for item in json_data:
        question = item['question']
        answer = item['gt_answer']
        key = f"{question}|{answer}"
        if key not in unique_questions:
            new_item = item.copy()
            try:
                new_item['image_path'] = get_random_image_path_cached(new_item['modality_type'], new_item['image_path'], available_images_cache)
            except Exception as e:
                continue
            unique_questions[key] = new_item
    print(f"Unique questions extracted: {len(unique_questions)}")
    return unique_questions

def extend_to_5000_questions(unique_questions: Dict[str, Dict]) -> List[Dict]:
    """
    Extend the number of questions to 5000 by selecting questions that cover all answer possibilities.
    
    Args:
        unique_questions (Dict[str, Dict]): Dictionary of unique questions
        
    Returns:
        List[Dict]: List of 5000 questions with good answer coverage
    """
    questions_list = list(unique_questions.values())
    current_count = len(questions_list)
    
    # Group questions by answer
    questions_by_answer = defaultdict(list)
    for q in questions_list:
        questions_by_answer[q['gt_answer']].append(q)
    
    # Calculate target questions per answer type
    num_answer_types = len(questions_by_answer)
    target_per_answer = 5000 // num_answer_types
    remaining_slots = 5000 % num_answer_types
    
    selected_questions = []
    
    # First, select questions to ensure coverage of all answer types
    for answer, questions in tqdm(questions_by_answer.items(), desc="Selecting questions by answer type"):
        # Calculate how many questions to select for this answer type
        num_to_select = min(target_per_answer + (1 if remaining_slots > 0 else 0), len(questions))
        remaining_slots -= 1
        
        # Randomly select questions for this answer type
        selected = random.sample(questions, num_to_select)
        selected_questions.extend(selected)
    
    # If we still need more questions, randomly select from remaining questions
    if len(selected_questions) < 5000:
        remaining_questions = [q for q in questions_list if q not in selected_questions]
        additional_needed = 5000 - len(selected_questions)
        additional_selected = random.sample(remaining_questions, additional_needed)
        selected_questions.extend(additional_selected)
    
    # Shuffle the final list to mix up answer types
    random.shuffle(selected_questions)
    return selected_questions[:5000]

def refill_question_ids(questions: List[Dict]) -> List[Dict]:
    """
    Refill question_ids with sequential IDs.
    
    Args:
        questions (List[Dict]): List of questions
        
    Returns:
        List[Dict]: List of questions with sequential IDs
    """
    for i, question in enumerate(questions):
        # Format the ID with leading zeros to maintain 4 digits
        question['question_id'] = f"PulmonaryChestMC_{i:04d}"
    return questions

def main():
    # Set random seed for reproducibility
    random.seed(42)
    print("Reading JSON file...")
    with open('../Original_open/RadImageNet.json', 'r') as f:
        data = json.load(f)
    print("JSON file loaded.")
    # Print all unique answers
    unique_answers = set(item['gt_answer'] for item in data)
    print("\nUnique answers in the original file:")
    for answer in sorted(unique_answers):
        print(f"- {answer}")
    print(f"\nTotal number of unique answers: {len(unique_answers)}")
    # Build image cache ONCE
    modality_to_dir = {
        "CT(Computed Tomography)": "RadImageNet/radiology_ai/CT",
        "MR (Mag-netic Resonance Imaging)": "RadImageNet/radiology_ai/MR",
        "ultrasound": "RadImageNet/radiology_ai/US"
    }
    used_filenames, available_images_cache = build_image_cache(modality_to_dir, data)
    # Extract unique questions (optimized)
    unique_questions = extract_unique_questions(data, available_images_cache)
    # Save the unique questions first
    print("Saving unique questions to file...")
    unique_questions_list = list(unique_questions.values())
    with open('RadImageNet/radimagenet_unique_questions_original.json', 'w') as f:
        json.dump(unique_questions_list, f, indent=4)
    print(f"\nOriginal unique questions have been saved to 'RadImageNet/radimagenet_unique_questions_original.json'")
    print(f"Number of unique questions: {len(unique_questions_list)}")
    # Extend to 5000 questions with answer coverage
    print("Extending to 5000 questions with answer coverage...")
    extended_questions = extend_to_5000_questions(unique_questions)
    # Refill question IDs sequentially
    print("Refilling question IDs...")
    final_questions = refill_question_ids(extended_questions)
    # Save the extended questions to a new JSON file
    print("Saving extended questions to file...")
    with open('RadImageNet/radimagenet_unique_questions.json', 'w') as f:
        json.dump(final_questions, f, indent=4)
    print(f"\nExtended questions have been saved to 'RadImageNet/radimagenet_unique_questions.json'")
    print(f"Extended to {len(final_questions)} questions")
    # Print distribution of answers in final dataset
    answer_distribution = defaultdict(int)
    for q in final_questions:
        answer_distribution[q['gt_answer']] += 1
    print("\nAnswer distribution in final dataset:")
    for answer, count in sorted(answer_distribution.items()):
        print(f"- {answer}: {count} questions")

if __name__ == "__main__":
    main()