


from __future__ import annotations

lowerCAmelCase__      		= '#'







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self)			->				None:
            _A      :       dict				    = {}


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = self._trie
            for char in text:
                   if char not in trie:
                          _A      :       List[str]				    = {}
                   _A      :       List[str]				    = trie[char]
            _A      :       Dict				    = True


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				tuple | list:
            _A      :       Optional[int]				    = self._trie
            for char in prefix:
                   if char in trie:
                          _A      :       Any				    = trie[char]
                   else:
                          return []
            return self._elements(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				tuple:
            _A      :       Any				    = []
            for c, v in d.items():
                   _A      :       Optional[Any]				    = [" "] if c == END else [(c + s) for s in self._elements(__lowerCamelCase)]
                   result.extend(__lowerCamelCase)
            return tuple(__lowerCamelCase)




lowerCAmelCase__      		= Trie()
lowerCAmelCase__      		= ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
    trie.insert_word(word)
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       _A      :       Tuple				    = trie.find_word(UpperCamelCase__						)
       return tuple(string + word for word in suffixes						)
def 							_UpperCAmelCase				():
       print(autocomplete_using_trie("de"						)						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



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
       snake_case_			  = 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''')
       snake_case_  ,		snake_case_			  = 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''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = 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)

