HVU_QA/generate_question.py

from __future__ import annotations

import argparse
import json
import os
import re
import sys
import threading
from pathlib import Path
from typing import Any

os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")


def raise_missing_dependency_error(exc: ModuleNotFoundError) -> None:
    root = Path(__file__).resolve().parent
    requirements = root / "requirements.txt"
    message = [
        f"Thiếu thư viện Python: {exc.name}",
        f"Interpreter hiện tại: {sys.executable}",
    ]
    if requirements.exists():
        message.extend(
            [
                "Cài đặt dependencies bằng lệnh:",
                f"{sys.executable} -m pip install -r {requirements}",
            ]
        )
    raise SystemExit("\n".join(message)) from exc


try:
    import torch
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
except ModuleNotFoundError as exc:
    raise_missing_dependency_error(exc)


APP_TITLE = "Mô hình sinh câu hỏi thường gặp"
TASK_PREFIX = "sinh câu hỏi"
QUESTION_LIMIT = 100
GENERATION_PASSES = (
    (0.9, 0.95, None, 1, 4),
    (1.0, 0.97, 16, 1, 5),
    (1.08, 0.99, 8, 2, 6),
)


def normalize_text(text: Any) -> str:
    return " ".join(str(text or "").split())


def unique_text(items: list[str]) -> list[str]:
    seen: set[str] = set()
    output: list[str] = []
    for item in items:
        value = normalize_text(item)
        key = value.lower()
        if key and key not in seen:
            seen.add(key)
            output.append(value)
    return output


def parse_question_count(value: Any, default: int = 5) -> int:
    try:
        parsed = int(value)
    except (TypeError, ValueError):
        parsed = default
    return max(1, min(parsed, QUESTION_LIMIT))


def format_questions(items: list[str]) -> str:
    if not items:
        return "Không sinh được câu hỏi phù hợp."
    return "\n".join(f"{index}. {item}" for index, item in enumerate(items, 1))


def resolve_model_dir(model_dir: str | Path, prefer_nested_model: bool = True) -> Path:
    model_root = Path(model_dir).expanduser().resolve()
    nested_candidates = [model_root / "best-model", model_root / "final-model"]
    candidates = [*nested_candidates, model_root] if prefer_nested_model else [model_root, *nested_candidates]
    for candidate in candidates:
        if candidate.is_dir() and (candidate / "config.json").exists():
            return candidate
    raise FileNotFoundError(f"Không tìm thấy thư mục mô hình hợp lệ: {model_root}")


def parse_dtype(value: str) -> torch.dtype:
    normalized = value.strip().lower()
    mapping = {
        "float16": torch.float16,
        "fp16": torch.float16,
        "float32": torch.float32,
        "fp32": torch.float32,
        "bfloat16": torch.bfloat16,
        "bf16": torch.bfloat16,
    }
    if normalized not in mapping:
        raise ValueError(f"Không hỗ trợ gpu_dtype={value}")
    return mapping[normalized]


class QuestionGenerator:
    def __init__(
        self,
        model_dir: str | Path = "t5-viet-qg-finetuned",
        task_prefix: str = TASK_PREFIX,
        max_source_length: int = 512,
        max_new_tokens: int = 64,
        device: str = "auto",
        cpu_threads: int | None = None,
        gpu_dtype: str = "auto",
        prefer_nested_model: bool = True,
    ) -> None:
        self.model_root = Path(model_dir).expanduser().resolve()
        self.model_dir = resolve_model_dir(model_dir, prefer_nested_model=prefer_nested_model)
        self.task_prefix = task_prefix
        self.max_source_length = max_source_length
        self.max_new_tokens = max_new_tokens
        self.requested_device = device
        self.cpu_threads = cpu_threads
        self.gpu_dtype = gpu_dtype
        self.prefer_nested_model = prefer_nested_model
        self.device: torch.device | None = None
        self.dtype: torch.dtype | None = None
        self.tokenizer = None
        self.model = None
        self._load_lock = threading.Lock()

    def _resolve_device(self) -> torch.device:
        requested = self.requested_device.lower()
        if requested == "cpu":
            return torch.device("cpu")
        if requested == "cuda":
            if not torch.cuda.is_available():
                raise RuntimeError("Bạn đã chọn device=cuda nhưng máy hiện tại không có CUDA.")
            return torch.device("cuda")
        return torch.device("cuda" if torch.cuda.is_available() else "cpu")

    def _resolve_dtype(self) -> torch.dtype:
        if self.device is None or self.device.type != "cuda":
            return torch.float32
        if self.gpu_dtype == "auto":
            if hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported():
                return torch.bfloat16
            return torch.float16
        return parse_dtype(self.gpu_dtype)

    def _configure_runtime(self) -> None:
        if self.device is None:
            return
        if self.device.type == "cpu":
            if self.cpu_threads:
                torch.set_num_threads(max(1, int(self.cpu_threads)))
                if hasattr(torch, "set_num_interop_threads"):
                    torch.set_num_interop_threads(max(1, min(int(self.cpu_threads), 4)))
            return

        if hasattr(torch.backends, "cuda") and hasattr(torch.backends.cuda, "matmul"):
            torch.backends.cuda.matmul.allow_tf32 = True
        if hasattr(torch.backends, "cudnn"):
            torch.backends.cudnn.allow_tf32 = True
            torch.backends.cudnn.benchmark = True

    def load(self) -> None:
        if self.model is not None and self.tokenizer is not None:
            return

        with self._load_lock:
            if self.model is not None and self.tokenizer is not None:
                return

            self.device = self._resolve_device()
            self.dtype = self._resolve_dtype()
            self._configure_runtime()

            model_kwargs: dict[str, Any] = {}
            if self.device.type == "cuda":
                model_kwargs["torch_dtype"] = self.dtype
                model_kwargs["low_cpu_mem_usage"] = True

            self.tokenizer = AutoTokenizer.from_pretrained(str(self.model_dir), use_fast=True)
            self.model = AutoModelForSeq2SeqLM.from_pretrained(str(self.model_dir), **model_kwargs)
            self.model.to(self.device)
            self.model.eval()

    def metadata(self) -> dict[str, Any]:
        active_device = self.device.type if self.device is not None else None
        predicted_device = "cuda" if torch.cuda.is_available() and self.requested_device != "cpu" else "cpu"
        return {
            "title": APP_TITLE,
            "model_root": str(self.model_root),
            "model_dir": str(self.model_dir),
            "requested_device": self.requested_device,
            "active_device": active_device,
            "predicted_device": predicted_device,
            "loaded": self.model is not None,
            "gpu_available": torch.cuda.is_available(),
            "gpu_dtype": None if self.dtype is None else str(self.dtype).replace("torch.", ""),
            "cpu_threads": torch.get_num_threads(),
        }

    def _candidate_answers(self, text: str, limit: int) -> list[str]:
        text = normalize_text(text)
        if not text:
            return []

        candidates: list[str] = []
        split_pattern = r"(?<=[.!?])\s+|\n+"
        for sentence in [normalize_text(part) for part in re.split(split_pattern, text) if normalize_text(part)]:
            if 3 <= len(sentence.split()) <= 30:
                candidates.append(sentence)
            for clause in (normalize_text(part) for part in re.split(r"\s*[,;:]\s*", sentence)):
                if 3 <= len(clause.split()) <= 20:
                    candidates.append(clause)

        if not candidates:
            words = text.split()
            candidates = [" ".join(words[: min(12, len(words))])] if words else [text]

        ranked = sorted(unique_text(candidates), key=lambda item: (abs(len(item.split()) - 10), len(item)))
        return ranked[:limit]

    def _build_prompt(self, context: str, answer: str) -> str:
        return f"{self.task_prefix}:\nngữ cảnh: {context}\nđáp án: {answer}"

    @torch.inference_mode()
    def _sample(self, context: str, answer: str, count: int, temperature: float, top_p: float) -> list[str]:
        if self.tokenizer is None or self.model is None or self.device is None:
            raise RuntimeError("Model chưa được load.")

        inputs = self.tokenizer(
            self._build_prompt(context, answer),
            return_tensors="pt",
            truncation=True,
            max_length=self.max_source_length,
        ).to(self.device)
        outputs = self.model.generate(
            **inputs,
            max_new_tokens=self.max_new_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            num_return_sequences=count,
            no_repeat_ngram_size=3,
            repetition_penalty=1.1,
        )
        questions: list[str] = []
        for token_ids in outputs:
            question = normalize_text(self.tokenizer.decode(token_ids, skip_special_tokens=True))
            if question:
                questions.append(question if question.endswith("?") else f"{question}?")
        return [question for question in unique_text(questions) if len(question.split()) >= 3]

    @torch.inference_mode()
    def _beam_search(self, context: str, answer: str, count: int) -> list[str]:
        if self.tokenizer is None or self.model is None or self.device is None:
            raise RuntimeError("Model chưa được load.")

        inputs = self.tokenizer(
            self._build_prompt(context, answer),
            return_tensors="pt",
            truncation=True,
            max_length=self.max_source_length,
        ).to(self.device)
        outputs = self.model.generate(
            **inputs,
            max_new_tokens=self.max_new_tokens,
            num_beams=max(4, count),
            num_return_sequences=min(count, 4),
            early_stopping=True,
            no_repeat_ngram_size=3,
            repetition_penalty=1.1,
        )
        questions: list[str] = []
        for token_ids in outputs:
            question = normalize_text(self.tokenizer.decode(token_ids, skip_special_tokens=True))
            if question:
                questions.append(question if question.endswith("?") else f"{question}?")
        return [question for question in unique_text(questions) if len(question.split()) >= 3]

    def generate(self, text: str, count: int = 5) -> list[str]:
        self.load()
        context = normalize_text(text)
        if not context:
            raise ValueError("Vui lòng nhập đoạn văn.")

        count = parse_question_count(count)
        pool = unique_text(
            self._candidate_answers(context, max(32, count * 5)) + [context[:180], context[:280], context]
        )
        output: list[str] = []
        seen: set[str] = set()

        for temperature, top_p, limit, rounds, floor in GENERATION_PASSES:
            answers = pool[:limit] if limit else pool
            for _ in range(rounds):
                for answer in answers:
                    remaining = count - len(output)
                    if remaining <= 0:
                        return output[:count]
                    sample_count = min(8, max(floor, remaining * 2))
                    for question in self._sample(context, answer, sample_count, temperature, top_p):
                        key = question.lower()
                        if key not in seen:
                            seen.add(key)
                            output.append(question)
                            if len(output) >= count:
                                return output[:count]

        for answer in pool[: min(8, len(pool))]:
            remaining = count - len(output)
            if remaining <= 0:
                break
            for question in self._beam_search(context, answer, remaining):
                key = question.lower()
                if key not in seen:
                    seen.add(key)
                    output.append(question)
                    if len(output) >= count:
                        break

        return output[:count]


def read_input_text(args: argparse.Namespace) -> str:
    if args.text:
        return args.text
    if args.input_file:
        return Path(args.input_file).read_text(encoding="utf-8")
    if sys.stdin.isatty():
        return input("Nhập đoạn văn cần sinh câu hỏi:\n").strip()
    return sys.stdin.read().strip()


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Sinh câu hỏi từ đoạn văn bằng model T5 fine-tuned.")
    parser.add_argument("--model_dir", default="t5-viet-qg-finetuned")
    parser.add_argument("--task_prefix", default=TASK_PREFIX)
    parser.add_argument("--max_source_length", type=int, default=512)
    parser.add_argument("--max_new_tokens", type=int, default=64)
    parser.add_argument("--num_questions", type=int, default=100)
    parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
    parser.add_argument("--cpu_threads", type=int, default=None)
    parser.add_argument("--gpu_dtype", default="auto")
    parser.add_argument("--text", default=None)
    parser.add_argument("--input_file", default=None)
    parser.add_argument("--output_format", choices=["text", "json"], default="text")
    return parser


def main() -> None:
    args = build_parser().parse_args()
    if hasattr(sys.stdout, "reconfigure"):
        sys.stdout.reconfigure(encoding="utf-8")
    generator = QuestionGenerator(
        model_dir=args.model_dir,
        task_prefix=args.task_prefix,
        max_source_length=args.max_source_length,
        max_new_tokens=args.max_new_tokens,
        device=args.device,
        cpu_threads=args.cpu_threads,
        gpu_dtype=args.gpu_dtype,
        prefer_nested_model=True,
    )
    text = read_input_text(args)
    questions = generator.generate(text, parse_question_count(args.num_questions))
    payload = {
        "text": normalize_text(text),
        "questions": questions,
        "formatted": format_questions(questions),
        "meta": generator.metadata(),
    }
    if args.output_format == "json":
        print(json.dumps(payload, ensure_ascii=False, indent=2))
        return
    print(payload["formatted"])


if __name__ == "__main__":
    main()