import kenlm
from text_normalizer import normalize

def document_perplexity(model, text):
    text = normalize(text)
    score = model.score(text)
    return 10 ** (-score / len(text.split()))

# Load the language model
model = kenlm.Model('../lm-v2.binary')

# Test the model
TEXT = """I thought I’d add a little bit of background. The previous discussion started from the result $P(B|AC) = K^{-1}P(B|C)P(A|BC) = K^{-1} P(AB|C)$ where $K=P(A|C).$ Although this is called Bayes’ theorem, the general form of it as stated here was actually first written down, not by Bayes but by Laplace."""

print(document_perplexity(model, TEXT))  # Should print out ~239