
"""simple docstring"""


_lowercase				 =					{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
_lowercase				 =					['''a''', '''b''', '''c''', '''d''', '''e''']






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  str						,   snake_case__ :  Optional[Any]  ):
	A     = start
	# add current to visited
	visited.append(snake_case__  )
	A     = edges[current]
	for neighbor in neighbors:
		# if neighbor not in visited, visit
		if neighbor not in visited:
			A     = topological_sort(snake_case__						,   snake_case__						,   snake_case__  )
    # if all neighbors visited add current to sort
	sort.append(snake_case__  )
	# if all vertices haven't been visited select a new one to visit
	if len(snake_case__  ) != len(snake_case__  ):
		for vertice in vertices:
			if vertice not in visited:
				A     = topological_sort(snake_case__						,   snake_case__						,   snake_case__  )
    # return sort
	return sort


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

"""simple docstring"""


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset