from dataclasses import dataclass

from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

from src.constants import MAX_ATTEMPTS


@dataclass
class ProgramState:
    current_word_index: int
    player_guesses: list
    player_points: int
    lm_guesses: list
    lm_points: int

    def correct_guess(self):
        # FIXME: not 1 for every point
        self.player_points += 1
        self.next_word()

    def next_word(self):
        self.current_word_index += 1
        self.player_guesses = []
        self.lm_guesses = []

    @property
    def player_guess_str(self):
        return "\n".join(self.player_guesses)

    def get_lm_guess_display(self, remaining_attempts: int) -> str:
        return "\n".join(map(tokenizer.decode, self.lm_guesses[: MAX_ATTEMPTS - remaining_attempts]))


STATE = ProgramState(
    current_word_index=20,
    player_guesses=[],
    lm_guesses=[],
    player_points=0,
    lm_points=0,
)

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
model.eval()