

"""simple docstring"""





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["image_processor", "tokenizer"]
					lowercase__				 =       "AutoImageProcessor"
					lowercase__				 =       "AutoTokenizer"







					def __init__(       self			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				str		):
						super().__init__(lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	self.image_processor







					def __call__(       self			:				str   ,lowercase_			:				Any=None   ,lowercase_			:				Any=None   ,lowercase_			:				Optional[Any]=None   ,**lowercase_			:				Dict		):

						if text is None and images is None:
							raise ValueError('''You have to specify either text or images. Both cannot be none.'''		)

						if text is not None:
							lowerCAmelCase__	:						List[str]  				=	self.tokenizer(lowercase_   ,return_tensors=lowercase_   ,**lowercase_		)

						if images is not None:
							lowerCAmelCase__	:						Optional[int]  				=	self.image_processor(lowercase_   ,return_tensors=lowercase_   ,**lowercase_		)

						if text is not None and images is not None:
							lowerCAmelCase__	:						Any  				=	image_features.pixel_values
							return encoding
						elif text is not None:
							return encoding
						else:
							return BatchEncoding(data=dict(**lowercase_		)   ,tensor_type=lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,*lowercase_			:				List[Any]   ,**lowercase_			:				Optional[Any]		):
						return self.tokenizer.batch_decode(*lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,*lowercase_			:				int   ,**lowercase_			:				Optional[int]		):
						return self.tokenizer.decode(*lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[Any]		):
						return ["input_ids", "attention_mask", "pixel_values"]







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)






