






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)










def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


