from __future__ import annotations

import argparse
import json
import re
import sys
from pathlib import Path

TAG_RE = re.compile(r"<[^>]+>")
DEFAULT_BASEER_OCR_MODEL = "AbdoTarek/Baseer-OCR-V1.0"


def clean_model_text(text: str) -> str:
    text = TAG_RE.sub("\n", text)
    text = re.sub(r"```(?:json|html|markdown|text)?", "", text, flags=re.IGNORECASE)
    text = text.replace("```", "").strip()
    try:
        payload = json.loads(text)
    except json.JSONDecodeError:
        payload = None
    if isinstance(payload, dict):
        full_text = payload.get("full_text") or payload.get("text") or payload.get("content")
        if isinstance(full_text, str):
            text = full_text
    lines = [line.strip() for line in text.splitlines() if line.strip()]
    return "\n".join(lines)


def main() -> None:
    if hasattr(sys.stdout, "reconfigure"):
        sys.stdout.reconfigure(encoding="utf-8", errors="replace")
    if hasattr(sys.stderr, "reconfigure"):
        sys.stderr.reconfigure(encoding="utf-8", errors="replace")

    parser = argparse.ArgumentParser(description="Extract Arabic text from page images with Baseer OCR.")
    parser.add_argument("--image-dir", required=True, type=Path)
    parser.add_argument("--out", required=True, type=Path)
    parser.add_argument("--model", default=DEFAULT_BASEER_OCR_MODEL)
    parser.add_argument("--max-new-tokens", type=int, default=2048)
    args = parser.parse_args()

    image_paths = sorted(args.image_dir.glob("*.png"))
    total = max(len(image_paths), 1)
    print(f"ARABIC_READER_PROGRESS 0 {total}", flush=True)

    import torch
    from PIL import Image
    from qwen_vl_utils import process_vision_info
    from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

    model = Qwen2VLForConditionalGeneration.from_pretrained(
        args.model,
        torch_dtype="auto",
        device_map="auto",
    ).eval()
    processor = AutoProcessor.from_pretrained(args.model)
    prompt = (
        "Extract ALL visible Arabic text from the document image. "
        "Return only JSON with a full_text field. Preserve the original reading order. "
        "Do not summarize, translate, or add explanations."
    )
    pieces: list[str] = []
    image_paths = sorted(args.image_dir.glob("*.png"))
    total = max(len(image_paths), 1)
    for index, image_path in enumerate(image_paths, start=1):
        image = Image.open(image_path).convert("RGB")
        messages = [
            {"role": "system", "content": [{"type": "text", "text": "You are an OCR assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": image},
                    {"type": "text", "text": prompt},
                ],
            },
        ]
        text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
        image_inputs, video_inputs = process_vision_info(messages)
        inputs = processor(
            text=[text],
            images=image_inputs,
            videos=video_inputs,
            padding=True,
            return_tensors="pt",
        ).to(model.device)
        with torch.inference_mode():
            output_ids = model.generate(**inputs, max_new_tokens=args.max_new_tokens, do_sample=False)
        result = processor.batch_decode(
            output_ids[:, inputs.input_ids.shape[1] :],
            skip_special_tokens=True,
        )[0]
        page_text = clean_model_text(result)
        if page_text:
            pieces.append(page_text)
        print(f"ARABIC_READER_PROGRESS {index} {total}", flush=True)

    args.out.parent.mkdir(parents=True, exist_ok=True)
    args.out.write_text("\n\n".join(pieces), encoding="utf-8")


if __name__ == "__main__":
    main()