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