"""Game logic for ValluvarAI guessing game."""
import random
from typing import Any, Final

from config import (
    _MAX_GENERATION_ATTEMPTS,
    _TEMP_INCREMENT,
    _MAX_TEMP,
    _MAX_SEED,
    _GENERATE_MAX_TOKENS,
    _BASE_TEMP,
)
from model_engine import generate, model, stoi, itos
from kural_engine import (
    _extract_tamil_couplet,
    _is_couplet_in_original,
    _is_valid_kural_structure,
    format_kural,
    _extract_kural_lines,
    _get_kurals_db,
    ORIGINAL_TEXT,
)

_SYNTHETIC_FALLBACKS: Final = (
    "கற்க கசடறக் கற்பவர் கற்றபின்\nநிற்க அதற்கு தக",
    "அன்பும் அறனும் உடைத்தாம் முகத்தில்\nஇன்பும் இறைவன் துணை",
    "அறம் செய்யும் உள்ளத்தார் வாழ்வின்\nஇன்பம் என்றும் தங்கும்",
    "கல்வி கற்பவர் கண்ணோட்டம் கொள்ளின்\nஅறிவு வளர்ந்து வரும்",
    "நல்லாறு நின்றார் நிலைமை சொல்லுங்கால்\nசொல்லுக சான்றோர் முன்",
)
_PROMPTS: Final = (
    "கடவுள் வாழ்த்து",
    "நட்பு",
    "அறன்",
    "வான் சிறப்பு",
    "அரசியல்",
    "பொருள்",
    "கல்வி",
    "காதல்",
)


def new_round() -> tuple[str, dict[str, Any] | None, str]:
    """Start a new guessing round. Returns Tamil couplet, state, and cleared reveal."""
    is_real = random.choice((True, False))

    if is_real:
        kural = random.choice(_get_kurals_db())
        tamil_only = f"{kural['tamil_1']}\n{kural['tamil_2']}"
        state = {"is_real": True, "data": kural}
        return tamil_only, state, ""

    prompt = random.choice(_PROMPTS)
    couplet: str | None = None
    ai_kural_raw = ""
    attempts = 0
    temp = _BASE_TEMP

    for attempt in range(_MAX_GENERATION_ATTEMPTS):
        attempts = attempt + 1
        temp = min(_BASE_TEMP + attempt * _TEMP_INCREMENT, _MAX_TEMP)
        seed = random.randint(1, _MAX_SEED)
        ai_kural_raw = generate(model, prompt, stoi, itos, max_new_tokens=_GENERATE_MAX_TOKENS, temperature=temp, seed=seed)

        couplet = _extract_tamil_couplet(ai_kural_raw)
        if couplet and not _is_couplet_in_original(couplet, ORIGINAL_TEXT):
            if _is_valid_kural_structure(couplet.split("\n")):
                break

    if not couplet or _is_couplet_in_original(couplet, ORIGINAL_TEXT):
        tamil_only = random.choice(_SYNTHETIC_FALLBACKS)
        english_lines: list[str] = []
        confidence = 50
        attempts = _MAX_GENERATION_ATTEMPTS
    else:
        formatted = format_kural(ai_kural_raw)
        tamil_lines, english_lines = _extract_kural_lines(formatted.split("\n"))
        tamil_only = "\n".join(tamil_lines[:2]) if len(tamil_lines) >= 2 else formatted
        confidence = max(20, 100 - (attempts - 1) * 6)

    state = {
        "is_real": False,
        "data": {
            "prompt": prompt,
            "temperature": temp,
            "attempts": attempts,
            "tamil_couplet": tamil_only,
            "english_lines": english_lines[:2],
            "confidence": confidence,
        },
    }
    return tamil_only, state, ""


def check_guess(guess: str, state: dict[str, Any] | None) -> str:
    """Check the user's guess and return reveal markdown."""
    if not state:
        return "⚠️ Click **Next Couplet** to start!"

    is_real = state["is_real"]
    correct = (guess == "valluvar" and is_real) or (guess == "ai" and not is_real)
    verdict = "✅ Correct!" if correct else "❌ Wrong!"
    data = state["data"]

    if is_real:
        return (
            f"## {verdict}\n\n"
            f"### 📖 Real Thirukkural #{data['number']}\n\n"
            f"**Chapter:** {data['chapter_tamil']} — *{data['chapter_english']}*\n\n"
            f"**Meaning:**\n> {data['english_1']}\n> {data['english_2']}"
        )

    eng_text = "\n> ".join(data["english_lines"]) if data["english_lines"] else "*(No translation generated)*"
    return (
        f"## {verdict}\n\n"
        f"### 🤖 AI Generated\n\n"
        f"**Prompt:** `{data['prompt']}`  \n"
        f"**Temperature:** {data['temperature']:.2f}  \n"
        f"**Generation attempts:** {data['attempts']}  \n"
        f"**Confidence:** {data['confidence']}%  \n\n"
        f"**Model's translation:**\n> {eng_text}"
    )