# evaluator.py
import torch
from torchmetrics.text.bleu import BLEUScore
from torchmetrics.text.rouge import ROUGEScore
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

class CodeEvaluator:
    def __init__(self, model_name="S-Dreamer/PyCodeT5"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.bleu = BLEUScore(n_gram=4).to(self.device) # use GPU if possible
        self.rouge = ROUGEScore().to(self.device)

    def evaluate(self, nl_input, target_code):
        self.model.eval() # Set model to evaluation mode
        with torch.no_grad(): # Disable gradient calculations
            inputs = self.tokenizer(nl_input, return_tensors="pt").to(self.device)
            outputs = self.model.generate(
                **inputs,
                max_length=512,
                num_beams=5,
                early_stopping=True,
            )
            generated_code = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

            bleu_score = self.bleu(generated_code, target_code)
            rouge_score = self.rouge(generated_code, target_code)

        return bleu_score, rouge_score

if __name__ == "__main__":
    evaluator = CodeEvaluator()
    nl_input = "Write a Python function to reverse a string."
    target_code = """def reverse_string(s):
    return s[::-1]
"""
    bleu_score, rouge_score = evaluator.evaluate(nl_input, target_code)
    print(f"BLEU score: {bleu_score}")
    print(f"ROUGE score: {rouge_score}")