# Step 1: Install Hugging Face Transformers
# !pip install transformers -q

#  Step 2: Import Required Libraries
from transformers import FNetForMaskedLM, FNetTokenizer, pipeline

#  Step 3: Load Pretrained FNet Model and Tokenizer
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
tokenizer = FNetTokenizer.from_pretrained("google/fnet-base")

# Step 4: Create a Fill-Mask Pipeline
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)

#  Step 5: Use the Model to Predict the Masked Word
sentence = "The sun rises in the [MASK]."
results = fill_mask(sentence)

#  Step 6: Print the Results
print(f"Input: {sentence}")
print("Predictions:")
for res in results:
    print(f">> {res['sequence']} (Score: {res['score']:.4f})")