





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
     _snake_case  :    Optional[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')
     _snake_case    , _snake_case  :    Optional[int]						    =  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')
          _snake_case  :    List[Any]						    =  rh.cluster(
              name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
          )
     else:
          _snake_case  :    int						    =  rh.cluster(
              name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     _snake_case  :    List[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)




import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DPTImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

