import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import sys

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")

for line in sys.stdin:
    line_splitted = line.split("\t")
    left_context = line_splitted[6].split(" ")[-1]
    right_context = line_splitted[7].split(" ")[0]

    word = "[MASK]"

    text = f"{left_context} {word} {right_context}"

    input_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt", max_length=512, truncation=True)

    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1][0]

    with torch.inference_mode():
        outputs = model(input_ids)
        predictions = outputs[0][0, mask_token_index].softmax(dim=0)

    top_k = 500
    top_k_tokens = torch.topk(predictions, top_k).indices.tolist()
    result = ''
    prob_sum = 0
    for token in top_k_tokens:
        word = tokenizer.convert_ids_to_tokens([token])[0]
        prob = predictions[token].item()
        prob_sum += prob 
        result += f"{word}:{prob} "
    diff = 1.0 - prob_sum
    result += f":{diff}"
    print(result)