


"""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
 UpperCAmelCase				=						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""")
 UpperCAmelCase			,  UpperCAmelCase				=						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""")
  UpperCAmelCase				=						rh.cluster(
      name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
  )
 else:
  UpperCAmelCase				=						rh.cluster(
      name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 UpperCAmelCase				=						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_segformer import SegformerImageProcessor


A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						None:





         """simple docstring"""




         warnings.warn(
             '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use SegformerImageProcessor instead.'''			,					A_			,					)
         super().__init__(*A_			,					**A_							)
