| import torch | |
| import torch.nn as nn | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| class CodeGenerator: | |
| def __init__(self, model_name): | |
| 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) | |
| def generate_code(self, nl_input, max_length=256, num_beams=4, early_stopping=True): | |
| inputs = self.tokenizer(nl_input, return_tensors="pt").to(self.device) | |
| outputs = self.model.generate( | |
| **inputs, | |
| max_length=max_length, | |
| num_beams=num_beams, | |
| early_stopping=early_stopping, | |
| ) | |
| generated_code = self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return generated_code | |
| if __name__ == "__main__": | |
| model_name = "S-Dreamer/PyCodeT5" | |
| generator = CodeGenerator(model_name) | |
| nl_input = "Write a Python function to calculate the factorial of a number." | |
| generated_code = generator.generate_code(nl_input) | |
| print(generated_code) |