text: # load libraries
  from comet_ml import Experiment
  from file_manager import FileManager
  from os.path import join
  from art_net import ArtNet
  import torch
  
  
  def main():
  
      hyper_params = {
          "learning_rate": 1,
          "style_weight": 10000,
          "content_weight": 1,
          "n_steps": 300
      }
  
      experiment = Experiment(api_key="a604AfX0S9Bmt6HdpMHxg9MCI",
                              project_name="style-transfer", workspace="polmonroig")
  
      experiment.log_parameters(hyper_params)
  
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      if torch.cuda.is_available():
          print("Currently using cuda device.")
  
      # define file paths and directories
      content_images_dir = "content_images/"
      style_images_dir = "style_images/"
      output_images_dir = "output_images/"
      content_image_name = "content_01.png"
      style_image_name = "style_01.jpg"
      output_image_path = join(output_images_dir,
                               content_image_name.split('.')[0] + "_" + style_image_name.split('.')[0] + ".jpg")
      content_image_path = join(content_images_dir, content_image_name)
      style_image_path = join(style_images_dir, style_image_name)
  
      # define image file manager
      max_shape = (800, 800)
      fileManager = FileManager(content_image_path, style_image_path,
                                device, max_shape)
  
      # read images
      content_image, style_image = fileManager.read_images()
      input_image = content_image.clone()
      model = ArtNet(device=device)
      output_image = model.train(hyper_params['content_weight'], hyper_params['style_weight'],
                                 hyper_params['n_steps'], content_image, style_image, input_image,
                                 hyper_params['learning_rate'], experiment)
  
      fileManager.save_image(output_image, output_image_path)
  
      experiment.log_image(output_image_path, content_image_name.split('.')[0] + "_" + style_image_name.split('.')[0])
  
  
  
  
  
  
  
  if __name__ == "__main__":
      main()
  