
from queue import Queue







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = Queue()
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [False] * len(a__   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [-1] * len(a__   )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = queue.get()
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE				:				List[str]				      = 1 - color[u]
                    queue.put(a__   )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(a__   )   ):
        if not visited[i]:
            queue.put(a__   )
            SCREAMING_SNAKE_CASE				:				Dict				      = 0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
      # Adjacency List of graph
      print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))






'''simple docstring'''





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






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''EfficientNetImageProcessor'''
      __lowercase					=	('''BertTokenizer''', '''BertTokenizerFast''')






      def __init__(self			,	a__			,	a__       )							-> Tuple:
         super().__init__(a__			,	a__       )






      def __call__(self			,	a__=None			,	a__=None			,	a__="max_length"			,	a__=6_4			,	a__=None			,	**a__       )							-> Any:
         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:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.tokenizer(
                a__			,	padding=a__			,	max_length=a__			,	return_tensors=a__			,	**a__       )

         if images is not None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processor(a__			,	return_tensors=a__			,	**a__       )

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






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer.model_input_names
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

