import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load model and tokenizer
model_name = "your_model_repo"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Ensure special tokens and preprocessing settings are applied
if tokenizer.special_tokens_map is None:
    tokenizer.special_tokens_map = {
        "bos_token": "<s>",
        "eos_token": "</s>",
        "unk_token": "<unk>",
        "sep_token": "</s>",
        "pad_token": "<pad>",
        "cls_token": "<s>",
        "mask_token": "<mask>"
    }
    tokenizer.save_pretrained(model_name)

preprocessor_config = {
    "do_lower_case": False,
    "max_length": 128,
    "truncation": True,
    "padding": "max_length"
}

# Define a function for text generation
def generate_code(prompt, max_length=128, temperature=0.7, top_p=0.9):
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True, max_length=preprocessor_config["max_length"])
    
    with torch.no_grad():
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_length=max_length,
            temperature=temperature,
            top_p=top_p,
            do_sample=True
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
if __name__ == "__main__":
    prompt = "def quicksort(arr):"
    generated_code = generate_code(prompt)
    print("Generated Code:\n", generated_code)