

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments









def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       [0] * len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       []
						SCREAMING_SNAKE_CASE_: str  		=       []
						SCREAMING_SNAKE_CASE_: List[str]  		=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(_UpperCAmelCase    )    ):
												if indegree[i] == 0:
																		queue.append(_UpperCAmelCase    )

						while queue:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       queue.pop(0    )
												cnt += 1
												topo.append(_UpperCAmelCase    )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(_UpperCAmelCase    )

						if cnt != len(_UpperCAmelCase    ):
												print("Cycle exists"    )
						else:
												print(_UpperCAmelCase    )


# Adjacency List of Graph
lowerCAmelCase							:  Any   	=						{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}



topological_sort(graph)






