





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


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      None:


							'''simple docstring'''

							warnings.warn(
							    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
							    " Please use VideoMAEImageProcessor instead." ,						__snake_case ,						)
							super().__init__(*__snake_case ,						**__snake_case				)