text: import os
  
  from transformers.file_utils import (
      cached_path,
      hf_bucket_url,
      is_remote_url,
  )
  from transformers.utils import logging
  
  logger = logging.get_logger(__name__)
  WEIGHTS_NAME = "mp_rank_00_model_states.pt"
  DEEPSPEED_CONFIG_NAME = "deepspeed_config.json"
  
  
  def download_model_files(pretrained_model_name_or_path):
      weights_path = download_file_from_hf(pretrained_model_name_or_path, WEIGHTS_NAME)
      deepspeed_config_path = download_file_from_hf(pretrained_model_name_or_path, DEEPSPEED_CONFIG_NAME)
      return weights_path, deepspeed_config_path
  
  
  def download_file_from_hf(pretrained_model_name_or_path: str, file_name: str) -> str:
      # Load model
      if pretrained_model_name_or_path is not None:
          if os.path.isdir(pretrained_model_name_or_path):
              if os.path.isfile(os.path.join(pretrained_model_name_or_path, file_name)):
                  # Load from a PyTorch checkpoint
                  archive_file = os.path.join(pretrained_model_name_or_path, file_name)
              else:
                  raise EnvironmentError(
                      "Error no file named {} found in directory {}".format(
                          file_name,
                          pretrained_model_name_or_path,
                      )
                  )
          elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
              archive_file = pretrained_model_name_or_path
          else:
              archive_file = hf_bucket_url(
                  pretrained_model_name_or_path,
                  filename=file_name,
                  revision=None,
                  mirror=None,
              )
  
          try:
              # Load from URL or cache if already cached
              resolved_archive_file = cached_path(
                  archive_file,
                  cache_dir=None,
                  force_download=False,
                  proxies=None,
                  resume_download=False,
                  local_files_only=False,
              )
          except EnvironmentError as err:
              logger.error(err)
              msg = (
                  f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                  f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on"
                  f"'https://huggingface.co/models'\n\n"
                  f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a"
                  f"file named one of {file_name}.\n\n"
              )
              raise EnvironmentError(msg)
  
          if resolved_archive_file == archive_file:
              logger.info("loading weights file {}".format(archive_file))
          else:
              logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
      else:
          resolved_archive_file = None
  
      return resolved_archive_file
  