"""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
 lowerCAmelCase_:					int     =					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_:					str     =					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[str]     =					rh.cluster(
      name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
  )
 else:
  lowerCAmelCase_:					List[str]     =					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)





"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            _UpperCAmelCase,				split=_UpperCAmelCase,				features=_UpperCAmelCase,				cache_dir=_UpperCAmelCase,				keep_in_memory=_UpperCAmelCase,				streaming=_UpperCAmelCase,				num_proc=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							path_or_paths if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) else {self.split: path_or_paths}
        lowercase__				 =							Text(
            cache_dir=_UpperCAmelCase,				data_files=_UpperCAmelCase,				features=_UpperCAmelCase,				**_UpperCAmelCase,				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.streaming:
             lowercase__				 =							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase,				download_mode=_UpperCAmelCase,				verification_mode=_UpperCAmelCase,				base_path=_UpperCAmelCase,				num_proc=self.num_proc,				)
             lowercase__				 =							self.builder.as_dataset(
                 split=self.split,				verification_mode=_UpperCAmelCase,				in_memory=self.keep_in_memory  )
        return dataset




