"""simple docstring"""




_a				:		Dict	   =       {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
_a				:		int	   =       ['a', 'b', 'c', 'd', 'e']





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	Optional[Any]     )	->				Optional[int]:
  _lowerCAmelCase						:	Any        =					start
  # add current to visited
  visited.append(__lowerCAmelCase     )
  _lowerCAmelCase						:	Any        =					edges[current]
  for neighbor in neighbors:
    # if neighbor not in visited, visit
    if neighbor not in visited:
      _lowerCAmelCase						:	Any        =					topological_sort(__lowerCAmelCase      ,__lowerCAmelCase      ,__lowerCAmelCase     )
    # if all neighbors visited add current to sort
  sort.append(__lowerCAmelCase     )
  # if all vertices haven't been visited select a new one to visit
  if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
    for vertice in vertices:
      if vertice not in visited:
        _lowerCAmelCase						:	Tuple        =					topological_sort(__lowerCAmelCase      ,__lowerCAmelCase      ,__lowerCAmelCase     )
    # return sort
  return sort


if __name__ == "__main__":
     _a				:		Tuple	   =       topological_sort('a', [], [])
     print(sort)





"""simple docstring"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




