

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			int )					->				Tuple:
 A__							:   str					       =		word.split()

 def justify(lowercase_:			list   ,				lowercase_:			int   ,				lowercase_:			int ) -> str:
  A__							:   Any					       =		max_width - width
  A__							:   str					       =		len(A__ )
  if len(A__ ) == 1:
   # if there is only word in line
   # just insert overall_spaces_count for the remainder of line
   return line[0] + " " * overall_spaces_count
  else:
   A__							:   int					       =		words_count - 1
   # num_spaces_between_words_list[i] : tells you to insert
   # num_spaces_between_words_list[i] spaces
   # after word on line[i]
   A__							:   List[str]					       =		spaces_to_insert_between_words * [
       overall_spaces_count // spaces_to_insert_between_words
   ]
   A__							:   Dict					       =		(
       overall_spaces_count % spaces_to_insert_between_words
   )
   # distribute spaces via round robin to the left words
   for i in range(A__ ):
    num_spaces_between_words_list[i] += 1
   A__							:   Union[str, Any]					       =		[]
   for i in range(A__ ):
    # add the word
    aligned_words_list.append(line[i] )
    # add the spaces to insert
    aligned_words_list.append(num_spaces_between_words_list[i] * """ """ )
   # just add the last word to the sentence
   aligned_words_list.append(line[-1] )
   # join the aligned words list to form a justified line
   return "".join(A__ )

 A__							:   Dict					       =		[]
 A__							:   Optional[Any]					       =		[]
 A__							:   Dict					       =		0
 for word in words:
  if width + len(A__ ) + len(A__ ) <= max_width:
   # keep adding words until we can fill out max_width
   # width = sum of length of all words (without overall_spaces_count)
   # len(word) = length of current word
   # len(line) = number of overall_spaces_count to insert between words
   line.append(A__ )
   width += len(A__ )
  else:
   # justify the line and add it to result
   answer.append(justify(A__   ,				A__   ,				A__ ) )
   # reset new line and new width
   A__       ,       A__							:   List[Any]					       =		[word], len(A__ )
 A__							:   Union[str, Any]					       =		max_width - width - len(A__ )
 answer.append(""" """.join(A__ ) + (remaining_spaces + 1) * """ """ )
 return answer


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

 testmod()






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



