



def 						UpperCamelCase      (  snake_case__			:       Tuple      )      ->	int:  # noqa: E741
   UpperCamelCase       :    str		      =      len(snake_case__      )
   UpperCamelCase       :    List[str]		      =      0
   UpperCamelCase       :    List[Any]		      =      [0] * n
   UpperCamelCase       :    str		      =      [False] * n
   UpperCamelCase       :    List[Any]		      =      [False] * n

   def dfs(snake_case__			:       Any			,	snake_case__			:       Optional[int]			,	snake_case__			:       str			,	snake_case__			:       int      ):
      if parent == root:
         out_edge_count += 1
      UpperCamelCase       :    Dict		      =      True
      UpperCamelCase       :    str		      =      at

      for to in l[at]:
         if to == parent:
            pass
         elif not visited[to]:
            UpperCamelCase       :    Dict		      =      dfs(snake_case__			,	snake_case__			,	snake_case__			,	snake_case__      )
            UpperCamelCase       :    Optional[Any]		      =      min(low[at]			,	low[to]      )

            # AP found via bridge
            if at < low[to]:
               UpperCamelCase       :    Tuple		      =      True
            # AP found via cycle
            if at == low[to]:
               UpperCamelCase       :    Tuple		      =      True
         else:
            UpperCamelCase       :    Optional[Any]		      =      min(low[at]			,	snake_case__      )
      return out_edge_count

   for i in range(snake_case__      ):
      if not visited[i]:
         UpperCamelCase       :    Any		      =      0
         UpperCamelCase       :    List[Any]		      =      dfs(snake_case__			,	snake_case__			,	-1			,	snake_case__      )
         UpperCamelCase       :    Optional[int]		      =      out_edge_count > 1

   for x in range(len(snake_case__      )      ):
      if is_art[x] is True:
         print(snake_case__      )


# Adjacency list of graph
__UpperCAmelCase			  =			{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}


compute_ap(data)






from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , 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=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              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   )

