






"""simple docstring"""







import warnings

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    =['image_processor', 'tokenizer']
       _SCREAMING_SNAKE_CASE    ='ViTImageProcessor'
       _SCREAMING_SNAKE_CASE    =('CLIPTokenizer', 'CLIPTokenizerFast')




       def __init__(   self:     Optional[int]				,		__A:     Optional[Any]=None				,		__A:     Dict=None				,		**__A:     Optional[int]    ):

            '''simple docstring'''

            a__          =  None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                     ''' instead.'''				,		__A				,		)
                 a__          =  kwargs.pop('''feature_extractor'''    )

            a__          =  image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                 raise ValueError('''You need to specify an `image_processor`.'''    )
            if tokenizer is None:
                 raise ValueError('''You need to specify a `tokenizer`.'''    )

            super().__init__(__A				,		__A    )




       def __call__(   self:     int				,		__A:     Any=None				,		__A:     Optional[Any]=None				,		__A:     Optional[Any]=None				,		__A:     Optional[Any]=None				,		**__A:     Dict    ):

            '''simple docstring'''

            if text is None and visual_prompt is None and images is None:
                 raise ValueError('''You have to specify either text, visual prompt or images.'''    )

            if text is not None and visual_prompt is not None:
                 raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''    )

            if text is not None:
                 a__          =  self.tokenizer(__A				,		return_tensors=__A				,		**__A    )

            if visual_prompt is not None:
                 a__          =  self.image_processor(__A				,		return_tensors=__A				,		**__A    )

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

            if visual_prompt is not None and images is not None:
                 a__          =  {
                     '''pixel_values''': image_features.pixel_values,
                     '''conditional_pixel_values''': prompt_features.pixel_values,
                 }
                 return encoding
            elif text is not None and images is not None:
                 a__          =  image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            elif visual_prompt is not None:
                 a__          =  {
                     '''conditional_pixel_values''': prompt_features.pixel_values,
                 }
                 return encoding
            else:
                 return BatchEncoding(data=dict(**__A    )				,		tensor_type=__A    )




       def  lowercase		(   self:     int				,		*__A:     Union[str, Any]				,		**__A:     int    ):

            '''simple docstring'''

            return self.tokenizer.batch_decode(*__A				,		**__A    )




       def  lowercase		(   self:     List[Any]				,		*__A:     Union[str, Any]				,		**__A:     Dict    ):

            '''simple docstring'''

            return self.tokenizer.decode(*__A				,		**__A    )




       @property
       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

            warnings.warn(
                '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,		__A				,		)
            return self.image_processor_class





       @property
       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

            warnings.warn(
                '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,		__A				,		)
            return self.image_processor










"""simple docstring"""







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												print(self.vertex    )
												for i in self.vertex:
																	print(__A				,		''' -> '''				,		''' -> '''.join([str(__A    ) for j in self.vertex[i]]    )    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if from_vertex in self.vertex:
																	self.vertex[from_vertex].append(__A    )
												else:
																	# else make a new vertex
																	a__          =  [to_vertex]




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  [False] * len(self.vertex    )

												# call the recursive helper function
												for i in range(len(self.vertex    )    ):
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )





							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     list    ):

												'''simple docstring'''

												a__          =  True

												print(__A				,		end=''' '''    )

												# Recur for all the vertices that are adjacent to this node
												for i in self.vertex:
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )


if __name__ == "__main__":
	__a       :	Any             = Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	print('DFS:')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3


