import torch
from transformers import BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer
import argparse
import sys

class AdvancedSummarizer:
    def __init__(self, model_name="facebook/bart-large-cnn"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = BartForConditionalGeneration.from_pretrained(model_name).to(self.device)
        self.tokenizer = BartTokenizer.from_pretrained(model_name)

    def summarize(self, text, max_length=150, min_length=50, length_penalty=2.0, num_beams=4):
        inputs = self.tokenizer([text], max_length=1024, return_tensors="pt", truncation=True)
        inputs = inputs.to(self.device)

        summary_ids = self.model.generate(
            inputs["input_ids"],
            num_beams=num_beams,
            max_length=max_length,
            min_length=min_length,
            length_penalty=length_penalty
        )

        summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        return summary

def main_summarizer():
    # Example usage
    summarizer = AdvancedSummarizer()
    text = """Your text here"""  # Replace with your actual text
    summary = summarizer.summarize(text)
    print("Summary:")
    print(summary)

class AdvancedTextGenerator:
    def __init__(self, model_name="gpt2-medium"):
        try:
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            print(f"Using device: {self.device}")
            self.model = GPT2LMHeadModel.from_pretrained(model_name).to(self.device)
            self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        except Exception as e:
            print(f"Error initializing the model: {e}")
            sys.exit(1)

    def generate_text(self, prompt, max_length=100, num_return_sequences=1, 
                      temperature=1.0, top_k=50, top_p=0.95, repetition_penalty=1.0):
        try:
            input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)

            output_sequences = self.model.generate(
                input_ids=input_ids,
                max_length=max_length + len(input_ids[0]),
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                do_sample=True,
                num_return_sequences=num_return_sequences,
            )

            generated_sequences = []
            for generated_sequence in output_sequences:
                text = self.tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
                total_sequence = text[len(self.tokenizer.decode(input_ids[0], clean_up_tokenization_spaces=True)):]
                generated_sequences.append(total_sequence)

            return generated_sequences
        except Exception as e:
            return [f"Error during text generation: {e}"]

def main_generator():
    parser = argparse.ArgumentParser(description="Advanced Text Generator")
    parser.add_argument("--prompt", type=str, help="Starting prompt for text generation")
    parser.add_argument("--max_length", type=int, default=100, help="Maximum length of generated text")
    parser.add_argument("--num_sequences", type=int, default=1, help="Number of sequences to generate")
    parser.add_argument("--temperature", type=float, default=1.0, help="Temperature for sampling")
    parser.add_argument("--top_k", type=int, default=50, help="Top-k sampling parameter")
    parser.add_argument("--top_p", type=float, default=0.95, help="Top-p sampling parameter")
    parser.add_argument("--repetition_penalty", type=float, default=1.0, help="Repetition penalty")

    args = parser.parse_args()

    generator = AdvancedTextGenerator()

    if args.prompt:
        prompt = args.prompt
    else:
        print("Please enter the prompt for text generation:")
        prompt = input().strip()

    generated_texts = generator.generate_text(
        prompt, 
        max_length=args.max_length,
        num_return_sequences=args.num_sequences,
        temperature=args.temperature,
        top_k=args.top_k,
        top_p=args.top_p,
        repetition_penalty=args.repetition_penalty
    )

    print("\nGenerated Text(s):")
    for i, text in enumerate(generated_texts, 1):
        print(f"\n--- Sequence {i} ---")
        print(text)

if __name__ == "__main__":
    main_summarizer()  # Call the summarizer main function
    main_generator()   # Call the text generator main function