import argparse
import json
import os
from collections import Counter


def canonical_answer(answers):
    if not answers:
        return ""
    counts = Counter(answers)
    best = max(counts.values())
    for answer in answers:
        if counts[answer] == best:
            return answer
    return answers[0]


def ensure_symlink(src, dst):
    if os.path.islink(dst) or os.path.exists(dst):
        return
    os.symlink(src, dst)


def convert_split(src_json, image_dir, out_jsonl):
    with open(src_json, "r") as f:
        data = json.load(f)["data"]

    with open(out_jsonl, "w") as writer:
        for item in data:
            answers = item.get("answers", [])
            record = {
                "image": os.path.join(image_dir, f"{item['image_id']}.jpg"),
                "question": item["question"],
                "question_id": item["question_id"],
                "answer": canonical_answer(answers),
            }
            writer.write(json.dumps(record) + "\n")
    return data


def build_val_questions(val_data, out_path):
    payload = {
        "questions": [
            {
                "image_id": item["image_id"],
                "question": item["question"],
                "question_id": item["question_id"],
            }
            for item in val_data
        ]
    }
    with open(out_path, "w") as f:
        json.dump(payload, f)


def build_val_annotations(val_data, out_path):
    payload = {
        "annotations": [
            {
                "image_id": item["image_id"],
                "question_id": item["question_id"],
                "answers": [{"answer": ans} for ans in item.get("answers", [])],
            }
            for item in val_data
        ]
    }
    with open(out_path, "w") as f:
        json.dump(payload, f)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--official-root", type=str, default="data/textvqa_official")
    parser.add_argument("--output-root", type=str, default="data/textvqa")
    args = parser.parse_args()

    official_root = args.official_root
    output_root = args.output_root
    os.makedirs(output_root, exist_ok=True)

    train_images_src = os.path.abspath(os.path.join(official_root, "train_images"))
    test_images_src = os.path.abspath(os.path.join(official_root, "test_images"))
    train_images_dst = os.path.join(output_root, "train_images")
    test_images_dst = os.path.join(output_root, "test_images")
    ensure_symlink(train_images_src, train_images_dst)
    ensure_symlink(test_images_src, test_images_dst)

    train_json = os.path.join(official_root, "TextVQA_0.5.1_train.json")
    val_json = os.path.join(official_root, "TextVQA_0.5.1_val.json")
    test_json = os.path.join(official_root, "TextVQA_0.5.1_test.json")

    train_data = convert_split(
        train_json,
        os.path.join(output_root, "train_images"),
        os.path.join(output_root, "textvqa_train.jsonl"),
    )
    val_data = convert_split(
        val_json,
        os.path.join(output_root, "train_images"),
        os.path.join(output_root, "textvqa_val.jsonl"),
    )
    _ = convert_split(
        test_json,
        os.path.join(output_root, "test_images"),
        os.path.join(output_root, "textvqa_test.jsonl"),
    )

    build_val_questions(val_data, os.path.join(output_root, "textvqa_val_questions.json"))
    build_val_annotations(val_data, os.path.join(output_root, "textvqa_val_annotations.json"))

    print(f"wrote {len(train_data)} train samples")
    print(f"wrote {len(val_data)} val samples")
    print(f"prepared TextVQA data under {output_root}")


if __name__ == "__main__":
    main()