# text_generation.py

import torch
import torch.nn as nn

class YourTextGenerationModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(YourTextGenerationModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.linear = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x):
        embedded = self.embedding(x)
        lstm_out, _ = self.lstm(embedded)
        output = self.linear(lstm_out)
        return output

    def generate_text(self, prompt):
        # Your text generation logic here
        # Replace this with your actual text generation code
        generated_text = "Generated text for: " + prompt
        return generated_text

if __name__ == "__main__":
    # Example usage of your text generation model
    vocab_size = 10000  # Replace with your actual vocabulary size
    embedding_dim = 128  # Replace with your desired embedding dimension
    hidden_dim = 256  # Replace with your desired hidden dimension

    your_model = YourTextGenerationModel(vocab_size, embedding_dim, hidden_dim)
    prompt = "Once upon a time"
    generated_text = your_model.generate_text(prompt)

    print("Input Prompt:", prompt)
    print("Generated Text:", generated_text)