text: import sys
  import os
  import logging
  import shutil
  from config.config import Configuration as config
  from dataloader.common import Dataloader
  from model.common import Model
  from train.common import Trainer
  from utils.util import load_vissl_weights
  import argparse
  
  
  def parse_arguments():
      """
      Parse the command line arguments
      """
      ap = argparse.ArgumentParser()
      ap.add_argument("-config", "--config_path", required=False, default="config.yml",
                      help="The path to the pipeline .yml configuration file.")
  
      args = vars(ap.parse_args())
  
      return args
  
  
  def main():
      """
      This method implements the main flow of the training pipeline.
      """
      # Parse the arguments
      args = parse_arguments()
      config_path = args["config_path"]  # Config path
      # Load the configuration file
      config.load_config(config_path)
      # Read the general configuration parameters
      output_directory = config.cfg["general"]["output_directory"]
      experiment_id = config.cfg["general"]["experiment_id"]
      model_checkpoints_directory_name = config.cfg["general"]["model_checkpoints_directory_name"]
      # Create the output and experiment directory
      if not os.path.exists(f"{output_directory}/{experiment_id}/{model_checkpoints_directory_name}"):
          os.makedirs(f"{output_directory}/{experiment_id}/{model_checkpoints_directory_name}")
      else:
          print(f"The directory {output_directory}/{experiment_id} already exits. Please delete the directory or change "
                f"the experiment_id in the configuration file.")
          sys.exit(1)
      # Copy the configuration file to experiment directory
      shutil.copyfile(config_path, f"{output_directory}/{experiment_id}/{config_path.split('/')[-1]}")
      # Configure the logger
      logging.basicConfig(level=logging.DEBUG,
                          format="%(asctime)s: %(name)-s: %(levelname)-s: %(message)s",
                          datefmt="%m-%d %H:%M",
                          filename=f"{output_directory}/{experiment_id}/{experiment_id}.log",
                          filemode="w")
      # Define a Handler which writes INFO messages
      console = logging.StreamHandler()
      console.setLevel(logging.INFO)
      # Set a format which is simpler for console use
      formatter = logging.Formatter('%(name)-s: %(levelname)-s: %(message)s')
      console.setFormatter(formatter)  # Tell the handler to use this format
      logging.getLogger().addHandler(console)  # Add the handler to the root logger
      # Create the dataloaders
      dataloader = Dataloader(config=config)
      train_loader, test_loader = dataloader.get_loader()
      # Create the model
      model = Model(config=config).get_model()
      # Load pre-trained VISSL weights if prompted
      try:
          vissl_checkpoints_path = config.cfg["model"]["vissl_weights_path"]
          if not len(vissl_checkpoints_path) == 0:
              model = load_vissl_weights(model, vissl_checkpoints_path)
      except Exception:
          pass
      # Create the trainer and run training
      warm_up_epochs = config.cfg["train"]["warm_up_epochs"]
      if warm_up_epochs > 0:
          logging.info(f"Starting warp-up training loop using "
                       f"{config.cfg['train']['warm_up_loss_function_path'].split('.')[-1]} for {warm_up_epochs} epochs.")
          trainer = Trainer(config=config, model=model, dataloader=train_loader, val_dataloader=test_loader,
                            warm_up=True).get_trainer()
          trainer.train_and_validate(start_epoch=1, end_epoch=warm_up_epochs)
      logging.info(f"Staring main training loop using {config.cfg['train']['class_loss_function_path'].split('.')[-1]} "
                   f"for {config.cfg['train']['epochs'] - warm_up_epochs} epochs.")
      trainer = Trainer(config=config, model=model, dataloader=train_loader, val_dataloader=test_loader).get_trainer()
      trainer.train_and_validate(start_epoch=warm_up_epochs + 1)
  
  
  if __name__ == "__main__":
      """
      This script calls the main() which implements the main flow of the training pipeline.
      """
      main()
  