import json
import os
import random
from pathlib import Path
import glob
import re
import argparse


def load_json(filepath):
    """
    Loads a JSON file .
    """
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            return json.load(f)
    except FileNotFoundError:
        print(f"Error: The file was not found at {filepath}")
        return None
    except json.JSONDecodeError as e:
        print(f"Error: The file at {filepath} is not a valid JSON file. Details: {e}")
        return None


def read_text_file(filepath):
    """
    Loads a prompt from a text file.
    """
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            return f.read().strip()
    except FileNotFoundError:
        print(f"Error: The file was not found at {filepath}")
        return None


def format_items_list(items, language):
    """
    Formats a list of item dictionaries (services) into a human-readable string.
    """
    if not items:
        return ""

    formatted_lines = []
    for item in items:
        if not isinstance(item, dict):
            continue
        parts = []
        desc = item.get("description")
        if desc is not None:
            parts.append(f"{desc}")
        qty = item.get("quantity")
        if qty is not None:
            qty_str = "Quantity" if language == "english" else "Quantité"
            parts.append(f"{qty_str}: {qty}")
        date = item.get("date_of_service")
        if date is not None:
            date_str = "Date" if language == "english" else "Date"
            parts.append(f"{date_str}: {date}")
        mandatory = item.get("mandatory_coverage")
        if mandatory is not None:
            amo_str = (
                "Mandatory Coverage"
                if language == "english"
                else "Couverture obligatoire"
            )
            parts.append(f"{amo_str}: {mandatory}")
        amount = item.get("amount")
        if amount is not None:
            amount_str = "Amount" if language == "english" else "Montant"
            parts.append(f"{amount_str}: {amount}")
        formatted_lines.append("- " + ", ".join(parts))
    return "\n".join(formatted_lines)


def get_conversational_answer(field, label_data, answer_bank, language):
    """
    Generates a complete conversational answer by selecting a template and filling it
    with the appropriate value from the label data.
    """
    if not isinstance(label_data, dict):
        return ""
    value = label_data.get(field)
    field_templates = answer_bank.get(field)

    if not field_templates:
        return str(value) if value is not None else ""

    if value is None:
        return random.choice(field_templates.get("null", {}).get(language, [""]))
    if field == "items":
        template = random.choice(field_templates[language])
        formatted_list_string = format_items_list(value, language)
        return template.format(value=formatted_list_string)
    if isinstance(value, bool):
        bool_key = str(value).lower()
        if bool_key in field_templates[language]:
            return random.choice(field_templates[language][bool_key])
        return str(value)
    if isinstance(field_templates[language], list):
        template = random.choice(field_templates[language])
        return template.format(value=value)
    return str(value) if value is not None else ""


# --- Conversations Generation for Label Data ---
def generate_vqa_conversations(
    labels_path,
    image_root,
    system_prompt_path,
    questions_path,
    answers_path,
    output_path,
    ratio=0.4,
):
    """
    Generates multiple conversational VQA pairs for each field in a label file,
    and handles multi-page documents.
    """
    all_data_entries = load_json(labels_path)
    system_prompt = read_text_file(system_prompt_path)
    question_bank = load_json(questions_path)
    answer_bank = load_json(answers_path)

    if (
        not all_data_entries
        or not system_prompt
        or not question_bank
        or not answer_bank
    ):
        print("Could not load one or more necessary files. Exiting.")
        return

    final_conversations = []

    # Process each entry in the main label file
    for entry in all_data_entries:
        label_data = entry.get("label")
        image_filename_prefix = entry.get("image")

        # Skip entries that are unlabeled, as we need the label to generate Q&A pairs
        if not label_data or not image_filename_prefix:
            continue

        # Get a list of all fields in the label data
        # all_fields = [field for field in label_data if isinstance(field, str) and field in question_bank]
        all_fields = list(question_bank.keys())
        # Determine how many questions to ask based on the available fields
        num_to_sample = max(1, int(len(all_fields) * ratio))
        # Randomly select fields to ask questions about
        fields_to_ask = random.sample(all_fields, num_to_sample)

        # Find all image files in the image_root that start with the prefix.
        # This handles cases like 'doc-1.jpg', 'doc-2.jpg', 'doc_scale.jpg' etc.
        prefix_stem = Path(image_filename_prefix).stem
        search_pattern = os.path.join(image_root, f"{prefix_stem}*")
        found_image_paths = sorted(glob.glob(search_pattern))

        if not found_image_paths:
            print(
                f"Warning: No images found for prefix '{prefix_stem}' in '{image_root}'. Skipping."
            )
            continue

        # Create a list of image dictionaries for the user message
        image_content_list = [
            {"type": "image", "image": path} for path in found_image_paths
        ]

        # --- Create a new conversation for EACH field in the label ---
        for field in fields_to_ask:
            if not isinstance(field, str):
                continue
            if field not in question_bank:
                continue

            language = random.choice(["english", "french"])

            # Get the question from the question bank
            question_text = random.choice(question_bank[field][language])

            # Get the conversational answer from the answer bank
            answer_text = get_conversational_answer(
                field, label_data, answer_bank, language
            )

            # --- Assemble the conversation in the desired format ---
            system_message = {"role": "system", "content": system_prompt}

            user_message = {
                "role": "user",
                # The content is the list of image dicts, followed by the text dict
                "content": image_content_list
                + [{"type": "text", "text": "<image>" * len(found_image_paths) + question_text}],
            }

            assistant_message = {"role": "assistant", "content": answer_text}

            conversation = [system_message, user_message, assistant_message]
            final_conversations.append(conversation)

    # Save the final list of conversations to the output file
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(final_conversations, f, indent=4, ensure_ascii=False)

    print(f"Success! Generated {len(final_conversations)} conversational VQA entries.")
    print(f"Formatted data saved to: {output_path}")

# --- Conversations Generation for Multi-Turn Dialogues ---
def generate_multiturn_conversations(
    labels_path,
    image_root,
    system_prompt_path,
    questions_path,
    answers_path,
    output_path,
):
    """
    Generates multi-turn conversational VQA pairs based on predefined field groups.
    """
    all_data_entries = load_json(labels_path)
    system_prompt = read_text_file(system_prompt_path)
    question_bank = load_json(questions_path)
    answer_bank = load_json(answers_path)

    if (
        not all_data_entries
        or not system_prompt
        or not question_bank
        or not answer_bank
    ):
        print("Could not load one or more necessary files. Exiting.")
        return

    # --- MODIFICATION: Define the field groupings for multi-turn conversations ---
    CONVERSATION_GROUPS = {
        "doctor_name": ["profession", "finess_number", "rpps_number", "adeli_number"],
        "beneficiary_name": ["beneficiary_dob", "security_number"],
        "bill_paid": ["mandatory_coverage", "complementary_coverage", "client_part", "amount_paid"],
    }

    final_conversations = []

    for entry in all_data_entries:
        label_data = entry.get("label")
        image_filename_prefix = entry.get("image")

        if not label_data or not image_filename_prefix:
            continue

        # Find all image files associated with this entry
        prefix_stem = Path(image_filename_prefix).stem
        search_pattern = os.path.join(image_root, f"{prefix_stem}*")
        found_image_paths = sorted(glob.glob(search_pattern))

        if not found_image_paths:
            continue

        image_content_list = [
            {"type": "image", "image": path} for path in found_image_paths
        ]

        # --- Create a multi-turn conversation for each group ---
        for main_field, related_fields in CONVERSATION_GROUPS.items():
            # Start a conversation only if the main field exists in the label
            if main_field not in label_data:
                continue

            conversation = []
            language = random.choice(["english", "french"])

            # 1. Add the System Prompt
            conversation.append({"role": "system", "content": system_prompt})

            # 2. First User Turn (with image)
            first_question = random.choice(question_bank[main_field][language])
            conversation.append({
                "role": "user",
                "content": image_content_list + [{"type": "text", "text": "<image>" * len(found_image_paths) + first_question}],
            })

            # 3. First Assistant Turn
            first_answer = get_conversational_answer(
                main_field, label_data, answer_bank, language
            )
            conversation.append({"role": "assistant", "content": first_answer})

            # 4. Follow-up Turns for related fields
            for follow_up_field in related_fields:
                if follow_up_field in label_data:
                    # Follow-up User Turn (text only)
                    follow_up_question = random.choice(question_bank[follow_up_field][language])
                    conversation.append({
                        "role": "user",
                        "content": [{"type": "text", "text": follow_up_question}],
                    })

                    # Follow-up Assistant Turn
                    follow_up_answer = get_conversational_answer(
                        follow_up_field, label_data, answer_bank, language
                    )
                    conversation.append({"role": "assistant", "content": follow_up_answer})
            
            final_conversations.append(conversation)


    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(final_conversations, f, indent=4, ensure_ascii=False)

    print(f"Success! Generated {len(final_conversations)} multi-turn VQA conversations.")
    print(f"Formatted data saved to: {output_path}")
    
# --- Conversations Generation for only Images ---
def generate_vq_question(
    image_root, system_prompt_path, questions_path, output_path, ratio=0.4
):
    """
    Generates conversational VQA pairs for each document based on images only (no labels).
    Each conversation contains a system and user message for each question in the question bank.
    """
    system_prompt = read_text_file(system_prompt_path)
    question_bank = load_json(questions_path)

    if not system_prompt or not question_bank:
        print("Could not load one or more necessary files. Exiting.")
        return

    # Find all images and group by prefix
    all_image_paths = sorted(
        glob.glob(os.path.join(image_root, "*.jpg"))
        + glob.glob(os.path.join(image_root, "*.png"))
        + glob.glob(os.path.join(image_root, "*.jpeg"))
    )
    prefix_to_images = {}
    for path in all_image_paths:
        if not os.path.isfile(path):
            continue
        stem = Path(path).stem
        # Remove suffixes like _1_scale, _2_scale, etc.
        prefix = re.sub(r"(_\d+(_scale)?)$", "", stem)
        prefix_to_images.setdefault(prefix, []).append(path)

    # Get a list of all possible fields from the question bank.
    all_fields = list(question_bank.keys())
    # Determine how many questions to ask based on the available fields
    num_to_sample = max(1, int(len(all_fields) * ratio))

    final_conversations = []

    for prefix, image_paths in prefix_to_images.items():
        image_content_list = [
            {"type": "image", "image": path} for path in sorted(image_paths)
        ]

        # Randomly select fields to ask questions about
        fields_to_ask = random.sample(all_fields, num_to_sample)

        for field in fields_to_ask:
            language = random.choice(["english", "french"])
            question_text = random.choice(question_bank[field][language])

            system_message = {"role": "system", "content": system_prompt}
            user_message = {
                "role": "user",
                "content": image_content_list
                + [{"type": "text", "text": "<image>" * len(image_paths) + question_text}],
            }
            conversation = [system_message, user_message]
            final_conversations.append(conversation)

    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(final_conversations, f, indent=4, ensure_ascii=False)

    print(
        f"Success! Generated {len(final_conversations)} image-only conversational VQA entries."
    )
    print(f"Formatted data saved to: {output_path}")

# --- Conversations Generation for Multi-Turn Questions (No Labels) ---
def generate_multiturn_vq_question(
    image_root, system_prompt_path, questions_path, output_path
):
    """
    Generates multi-turn, question-only conversational prompts for each document.
    """
    system_prompt = read_text_file(system_prompt_path)
    question_bank = load_json(questions_path)

    if not system_prompt or not question_bank:
        print("Could not load one or more necessary files. Exiting.")
        return

    # --- MODIFICATION: Define the same field groupings ---
    CONVERSATION_GROUPS = {
        "doctor_name": ["profession", "finess_number", "rpps_number", "adeli_number"],
        "beneficiary_name": ["beneficiary_dob", "security_number"],
        "bill_paid": ["mandatory_coverage", "complementary_coverage", "client_part", "amount_paid"],
    }

    # Find all images and group by prefix
    all_image_paths = sorted(
        glob.glob(os.path.join(image_root, "*.jpg"))
        + glob.glob(os.path.join(image_root, "*.png"))
        + glob.glob(os.path.join(image_root, "*.jpeg"))
    )
    prefix_to_images = {}
    for path in all_image_paths:
        if not os.path.isfile(path):
            continue
        stem = Path(path).stem
        prefix = re.sub(r"(_\d+(_scale)?)$", "", stem)
        prefix_to_images.setdefault(prefix, []).append(path)

    final_conversations = []

    for prefix, image_paths in prefix_to_images.items():
        image_content_list = [
            {"type": "image", "image": path} for path in sorted(image_paths)
        ]

        # --- Create a multi-turn conversation for each group ---
        for main_field, related_fields in CONVERSATION_GROUPS.items():
            conversation = []
            language = random.choice(["english", "french"])

            # 1. Add the System Prompt
            conversation.append({"role": "system", "content": system_prompt})

            # 2. First User Turn (with image)
            first_question = random.choice(question_bank[main_field][language])
            conversation.append({
                "role": "user",
                "content": image_content_list + [{"type": "text", "text": "<image>" * len(image_paths) + first_question}],
            })

            # 3. Follow-up User Turns (text only)
            for follow_up_field in related_fields:
                if follow_up_field in question_bank:
                    follow_up_question = random.choice(question_bank[follow_up_field][language])
                    conversation.append({
                        "role": "user",
                        "content": [{"type": "text", "text": follow_up_question}],
                    })
            
            final_conversations.append(conversation)

    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(final_conversations, f, indent=4, ensure_ascii=False)

    print(f"Success! Generated {len(final_conversations)} multi-turn VQA questions.")
    print(f"Formatted data saved to: {output_path}")

# --- Main Execution Block ---
if __name__ == "__main__":
    
    parser = argparse.ArgumentParser(description="Generate VQA conversations from label data.")
    parser.add_argument("--image_root", type=str, default="/home/nguyendc/docai_dataset/factures/distill_data/docai_mgp_facture_v2_0", help="Root directory containing images.")
    parser.add_argument("--labels", type=str, default="/home/nguyendc/docai_dataset/factures/distill_data/docai_mgp_facture_v2_0/label_data.json", help="Path to the label data JSON file.")
    parser.add_argument("--system_prompt", type=str, default="/home/nguyendc/phong-dev/distillation/easydistill/mmkd/dev-vqa/qa_bank/unstructured_prompt.txt", help="Path to the system prompt text file.")
    parser.add_argument("--questions", type=str, default="/home/nguyendc/phong-dev/distill/prompt/question_bank.json", help="Path to the question bank JSON file.")
    parser.add_argument("--answers", type=str, default="/home/nguyendc/phong-dev/distill/prompt/answer_bank.json", help="Path to the answer bank JSON file.")
    parser.add_argument("--output", type=str, default="/home/nguyendc/phong-dev/distillation/data/vqa_label.json", help="Path to save the output VQA conversations JSON file.")
    parser.add_argument("--ratio", type=float, default=0.4, help="Ratio of fields to sample for questions (default: 0.4).")
    args = parser.parse_args()


    # Single-turn, field-by-field conversations WITH labels
    generate_vqa_conversations(args.labels, args.image_root, args.system_prompt, args.questions, args.answers, args.output, args.ratio)
    
    # Use this for multi-turn conversations WITH labels based on field groups
    # generate_multiturn_conversations(args.labels, args.image_root, args.system_prompt, args.questions, args.answers, args.output)
    
    # Use this for generating question-only prompts for unlabeled images
    # generate_vq_question(args.image_root, args.system_prompt, args.questions, args.output, args.ratio)
    
    # Use this for multi-turn question-only prompts for unlabeled images
    # generate_multiturn_vq_question(args.image_root, args.system_prompt, args.questions, args.output)
