'''simple docstring'''






import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 a_					:    Union[str, Any]          =    argparse.ArgumentParser()
 parser.add_argument("--user", type=str, default="ubuntu")
 parser.add_argument("--host", type=str, default="localhost")
 parser.add_argument("--key_path", type=str, default=None)
 parser.add_argument("--instance", type=str, default="V100:1")
 parser.add_argument("--provider", type=str, default="cheapest")
 parser.add_argument("--use_spot", type=bool, default=False)
 parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
 a_					:    List[Any]          =    parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError("Cannot specify both BYO and on-demand cluster args")
  a_					:    List[str]          =    rh.cluster(
      name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
  )
 else:
  a_					:    Tuple          =    rh.cluster(
      name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 a_					:    Optional[Any]          =    args.example.rsplit("/", 1)[0]

 # Set up remote environment
 cluster.install_packages(["pip:./"])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
 cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([f'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

