# from transformers import MT5Tokenizer, MT5ForConditionalGeneration

# local_model_path = "./t5m_pocet"
# tokenizer = MT5Tokenizer.from_pretrained(local_model_path, legacy=False)
# model = MT5ForConditionalGeneration.from_pretrained(local_model_path)


model_directory = './t5m_pocet/model_directory'

from transformers import MT5ForConditionalGeneration, MT5Tokenizer
import torch

tokenizer = MT5Tokenizer(sp_model_kwargs={"model_file": f"{model_directory}/spiece.model"})

# local_model_path = "./t5m_pocet"
# tokenizer = MT5Tokenizer.from_pretrained(local_model_path, legacy=False)
# model = MT5ForConditionalGeneration.from_pretrained(local_model_path)



# Загрузка токенизатора и модели mT5
# model_name = "google/mt5-small"
# tokenizer = MT5Tokenizer.from_pretrained(model_name)
# model = MT5ForConditionalGeneration.from_pretrained(model_name)

# # Пример входных данных
# context = "Контекст, на основе которого нужно ответить на вопрос."
# question = "Какой вопрос нужно задать?"

# # Форматирование входных данных для модели
# input_text = f"question: {question} context: {context}"
# input_ids = tokenizer(input_text, return_tensors="pt").input_ids

# # Генерация ответа
# outputs = model.generate(input_ids)
# answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

# print(answer)