import traceback
from queue import Queue
from threading import Thread
import collections.abc

import torch
from transformers import StoppingCriteria


class StoppingCriteriaSub(StoppingCriteria):

    def __init__(self, stops=[], encounters=[]):
        super().__init__()
        assert len(stops) % len(encounters) == 0, "Number of stops and encounters must match"
        self.encounters = encounters
        self.stops = [stop.to("cuda") for stop in stops]
        self.num_stops = [0] * len(stops)

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stopi, stop in enumerate(self.stops):
            if torch.all((stop == input_ids[0][-len(stop):])).item():
                self.num_stops[stopi] += 1
                if self.num_stops[stopi] >= self.encounters[stopi % len(self.encounters)]:
                    return True
        # print("Tokens: %s" % input_ids[0].cpu().numpy(), flush=True)
        # print("Stop Tokens: %s" % [x.cpu().numpy() for x in self.stops], flush=True)
        return False