


'''simple docstring'''







class A__	:






      def __init__(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :int     )	-> None:




             '''simple docstring'''
             _a   :			str					 =data
             _a   :			int
             _a   :			Node


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Node  )  ->      None:
       _a   :			Any					 =0
       _a   :			Optional[int]					 =x


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Node   ,_UpperCAmelCase     :     Node  )  ->      None:
       _a ,   _a   :			Union[str, Any]					 =find_set(_UpperCAmelCase  ), find_set(_UpperCAmelCase  )
       if x == y:
              return

       elif x.rank > y.rank:
              _a   :			Optional[int]					 =x
       else:
              _a   :			int					 =y
              if x.rank == y.rank:
                     y.rank += 1


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Node  )  ->      Node:
       if x != x.parent:
              _a   :			Union[str, Any]					 =find_set(x.parent  )
       return x.parent


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Node  )  ->      set:
       _a   :			Optional[int]					 =({0, 1, 2}, {3, 4, 5})
       for s in sets:
              if node.data in s:
                     return s
       _a   :			Dict					 =F"{node.data} is not in {sets}"
       raise ValueError(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _a   :			Optional[Any]					 =[Node(_UpperCAmelCase  ) for i in range(6  )]
       for v in vertex:
              make_set(_UpperCAmelCase  )

       union_set(vertex[0]   ,vertex[1]  )
       union_set(vertex[1]   ,vertex[2]  )
       union_set(vertex[3]   ,vertex[4]  )
       union_set(vertex[3]   ,vertex[5]  )

       for nodea in vertex:
              for nodea in vertex:
                     if find_python_set(_UpperCAmelCase  ).isdisjoint(find_python_set(_UpperCAmelCase  )  ):
                            assert find_set(_UpperCAmelCase  ) != find_set(_UpperCAmelCase  )
                     else:
                            assert find_set(_UpperCAmelCase  ) == find_set(_UpperCAmelCase  )


if __name__ == "__main__":
   test_disjoint_set()






'''simple docstring'''







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







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             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:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

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

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






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


