import argparse
import copy
import json
import os
import random
import re
import sys
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import numpy as np


proj_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, proj_path)

from lib_code import attack_defense_tools as attack_tools  # noqa: E402
from lib_code.attack_defense_tools import ignore, naive, none  # noqa: E402


RANDOM_SEED = 42
SOURCE_FILES = {
    "squad": "1_raw_dataset/topicattack/data/crafted_instruction_data_squad_injection_qa.json",
    "tri": "1_raw_dataset/topicattack/data/crafted_instruction_data_tri_injection_qa.json",
}
SYSTEM_PROMPT_FILE = "1_raw_dataset/topicattack/prompts/generator_system_prompt.txt"

CONFIGS = (
    "prompt_based_separator",
    "native_tool_response_only",
    "native_tool_empty_query",
)
TRAIN_ATTACK_SPECS = (
    ("none", "none"),
    ("naive", "left"),
    ("naive", "right"),
    ("ignore", "left"),
    ("ignore", "right"),
)
ATTACK_MAP: Dict[str, Callable] = {
    "none": none,
    "naive": naive,
    "ignore": ignore,
}
SIDE_MAP = {
    "none": "end",
    "left": "start",
    "right": "end",
}


def load_json(path: Path):
    with path.open("r", encoding="utf-8") as f:
        return json.load(f)


def load_text(path: Path) -> str:
    with path.open("r", encoding="utf-8") as f:
        return f.read()


def clean_input_text(text: str) -> str:
    text = text.replace("[DOC]", "").replace("[TLE]", "").replace("[PAR]", "")
    text = text.replace("\n", "")
    return re.sub(" +", " ", text).strip()


def normalize_item(item: dict) -> dict:
    normalized = copy.deepcopy(item)
    normalized["input"] = clean_input_text(normalized.get("input") or "")
    return normalized


def apply_attack(item: dict, attack: str, side_label: str) -> dict:
    attack_fn = ATTACK_MAP[attack]
    attacked = copy.deepcopy(item)
    attacked["add_mark"] = "inst"
    return attack_fn(attacked, side=SIDE_MAP[side_label], model=None, additional_injections=[])


def build_messages(item: dict, system_prompt: str, config: str) -> List[dict]:
    instruction = item["instruction"]
    input_data = item.get("input") or ""

    if config == "prompt_based_separator":
        user_input = (
            "<Instruction Area>\n{instruction}\n<\\Instruction Area>\n\n"
            "<Data Area>\n{data}\n<\\Data Area>\n"
        ).format(instruction=instruction, data=input_data)
        return [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_input},
        ]

    if config == "native_tool_response_only":
        return [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": instruction},
            {"role": "tool", "content": input_data},
        ]

    if config == "native_tool_empty_query":
        tool_call_id = "call_context"
        return [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": instruction},
            {
                "role": "assistant",
                "content": "",
                "tool_calls": [
                    {
                        "id": tool_call_id,
                        "type": "function",
                        "function": {"name": "get_context", "arguments": "{}"},
                    }
                ],
            },
            {"role": "tool", "tool_call_id": tool_call_id, "content": input_data},
        ]

    raise ValueError(f"Unsupported config: {config}")


def iter_records(
    source: str,
    config: str,
    data: Iterable[dict],
    system_prompt: str,
    data_size: int,
):
    if data_size > 0:
        data = list(data)[:data_size]

    for item_idx, raw_item in enumerate(data):
        base_item = normalize_item(raw_item)
        for attack, side_label in TRAIN_ATTACK_SPECS:
            attacked_item = apply_attack(base_item, attack, side_label)
            yield {
                "id": f"{source}_{item_idx:04d}_{config}_{attack}_{side_label}",
                "source": source,
                "config": config,
                "attack": attack,
                "side": side_label,
                "original_index": item_idx,
                "instruction": base_item["instruction"],
                "input": attacked_item.get("input") or "",
                "expected_output": base_item.get("output", ""),
                "injection": base_item.get("injection", ""),
                "injection_output": base_item.get("injection_output", ""),
                "messages": build_messages(attacked_item, system_prompt, config),
            }


def generate_single_turn(base_dir: Path, output_dir: Path, data_size: int = -1):
    random.seed(RANDOM_SEED)
    system_prompt = load_text(base_dir / SYSTEM_PROMPT_FILE)
    output_dir.mkdir(parents=True, exist_ok=True)

    for source, rel_path in SOURCE_FILES.items():
        data = load_json(base_dir / rel_path)
        for config in CONFIGS:
            attack_tools._random = random.Random(42)
            attack_tools.np_random = np.random.default_rng(seed=123)
            records = list(iter_records(source, config, data, system_prompt, data_size))
            out_path = output_dir / f"{source}_{config}.json"
            with out_path.open("w", encoding="utf-8") as f:
                json.dump(records, f, ensure_ascii=False, indent=2)
            print(f"Saved {len(records)} records -> {out_path}")


def main():
    parser = argparse.ArgumentParser(description="Generate single-turn training message JSON files.")
    parser.add_argument(
        "--base-dir",
        default="/home/hujk/gitrs/Paper2026/SortedCode2",
        help="SortedCode2 base directory. Raw datasets are read from {base-dir}/1_raw_dataset.",
    )
    parser.add_argument(
        "--output-dir",
        default="/home/hujk/gitrs/Paper2026/SortedCode2/3-1_model_training_data_gen/single_turn",
        help="Directory for the six generated source x config JSON files.",
    )
    parser.add_argument("--data-size", type=int, default=-1, help="Debug only: limit source rows.")
    args = parser.parse_args()

    generate_single_turn(Path(args.base_dir), Path(args.output_dir), data_size=args.data_size)


if __name__ == "__main__":
    main()