from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
import math

MODEL_NAME = "microsoft/deberta-v3-base"

print(f"Loading {MODEL_NAME}...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForMaskedLM.from_pretrained(MODEL_NAME)
model.eval()

text = "This is a simple sentence written by a human."

inputs = tokenizer(text, return_tensors="pt")
labels = inputs.input_ids.clone()

with torch.no_grad():
    outputs = model(inputs.input_ids, labels=labels)
    loss = outputs.loss
    ppl = torch.exp(loss)

print(f"Text: {text}")
print(f"Loss: {loss.item()}")
print(f"Perplexity: {ppl.item()}")

if ppl.item() < 1.5:
    print("\nWARNING: Perplexity is extremely low (~1.0).")
    print("This indicates the model is predicting tokens it can already see (Identity/Auto-encoder behavior).")
    print("For proper PPL/Pseudo-PPL, we must mask the input tokens.")