




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_UpperCAmelCase	:						Optional[Any]			=     TypeVar("T")




class    __lowerCAmelCase       (							Generic[T]):






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					T				):
         lowercase    :Union[str, Any]      	=						data
         lowercase    :Node[T] | None      	=						None






       def __str__(   self:					str				):
         return F"{self.data}"







class    __lowerCAmelCase       (							Generic[T]):






       def __init__(   self:					Optional[int]				):
         lowercase    :Node[T] | None      	=						None






       def __iter__(   self:					List[Any]				):
         lowercase    :str      	=						self.top
         while node:
           yield node.data
           lowercase    :Optional[Any]      	=						node.next






       def __str__(   self:					Dict				):
         return "->".join([str(_lowerCAmelCase				) for item in self]				)






       def __len__(   self:					int				):
         return len(tuple(iter(self				)				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return self.top is None






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					T				):
         lowercase    :Optional[Any]      	=						Node(_lowerCAmelCase				)
         if not self.is_empty():
           lowercase    :Optional[int]      	=						self.top
         lowercase    :Tuple      	=						node






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         if self.is_empty():
           raise IndexError("pop from empty stack"				)
         assert isinstance(self.top       ,					_lowerCAmelCase				)
         lowercase    :Tuple      	=						self.top
         lowercase    :Dict      	=						self.top.next
         return pop_node.data






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         if self.is_empty():
           raise IndexError("peek from empty stack"				)

         assert self.top is not None
         return self.top.data






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Union[str, Any]      	=						None


if __name__ == "__main__":
     from doctest import testmod

     testmod()









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	:						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")
     _UpperCAmelCase			,			_UpperCAmelCase	:						Dict			=     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	:						List[Any]			=     rh.cluster(
              name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
          )
     else:
          _UpperCAmelCase	:						Tuple			=     rh.cluster(
              name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     _UpperCAmelCase	:						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)



