from transformers import AutoTokenizer, AutoModelForMaskedLM
from transformers import pipeline
import random
from nltk.corpus import stopwords

# Masking Model
def mask_non_stopword(sentence):
    stop_words = set(stopwords.words('english'))
    words = sentence.split()
    non_stop_words = [word for word in words if word.lower() not in stop_words]
    if not non_stop_words:
        return sentence
    word_to_mask = random.choice(non_stop_words)
    masked_sentence = sentence.replace(word_to_mask, '[MASK]', 1)
    return masked_sentence

# Load tokenizer and model for masked language model
tokenizer = AutoTokenizer.from_pretrained("bert-large-cased-whole-word-masking")
model = AutoModelForMaskedLM.from_pretrained("bert-large-cased-whole-word-masking")
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)

def mask(sentence):
    predictions = fill_mask(sentence)
    masked_sentences = [predictions[i]['sequence'] for i in range(len(predictions))]
    return masked_sentences