


'''simple docstring'''


import warnings

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






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Dict          =     ["image_processor", "tokenizer"]
			lowerCAmelCase :	Tuple          =     "CLIPImageProcessor"
			lowerCAmelCase :	Dict          =     ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")




			def __init__(		self  :     int	,  lowerCamelCase__  :     Tuple=None	,  lowerCamelCase__  :     Any=None	,  **lowerCamelCase__  :     List[str] )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."	,  lowerCamelCase__	,  )
							_UpperCAmelCase		:       List[str]								=      kwargs.pop("feature_extractor" )

					_UpperCAmelCase		:       Optional[Any]								=      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__(lowerCamelCase__	,  lowerCamelCase__ )




			def __call__(		self  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     int=None	,  lowerCamelCase__  :     int=None	,  **lowerCamelCase__  :     List[str] )   ->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:
							_UpperCAmelCase		:       Union[str, Any]								=      self.tokenizer(lowerCamelCase__	,  return_tensors=lowerCamelCase__	,  **lowerCamelCase__ )

					if images is not None:
							_UpperCAmelCase		:       Union[str, Any]								=      self.image_processor(lowerCamelCase__	,  return_tensors=lowerCamelCase__	,  **lowerCamelCase__ )

					if text is not None and images is not None:
							_UpperCAmelCase		:       Union[str, Any]								=      image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					else:
							return BatchEncoding(data=dict(**lowerCamelCase__ )	,  tensor_type=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Any	,  *lowerCamelCase__  :     Any	,  **lowerCamelCase__  :     Any )   ->str:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     int	,  *lowerCamelCase__  :     Union[str, Any]	,  **lowerCamelCase__  :     List[Any] )   ->Tuple:



					'''simple docstring'''







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






			@property
			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self.tokenizer.model_input_names
					_UpperCAmelCase		:       str								=      self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):  # noqa: E741
		_UpperCAmelCase		:       List[str]								=      len(__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      0
		_UpperCAmelCase		:       List[str]								=      [0] * n
		_UpperCAmelCase		:       int								=      [False] * n
		_UpperCAmelCase		:       Dict								=      [False] * n

		def dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
				if parent == root:
						out_edge_count += 1
				_UpperCAmelCase		:       List[Any]								=      True
				_UpperCAmelCase		:       str								=      at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								_UpperCAmelCase		:       List[str]								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
								_UpperCAmelCase		:       Tuple								=      min(low[at]      , low[to]   )

								# AP found via bridge
								if at < low[to]:
										_UpperCAmelCase		:       Dict								=      True
								# AP found via cycle
								if at == low[to]:
										_UpperCAmelCase		:       Dict								=      True
						else:
								_UpperCAmelCase		:       Optional[int]								=      min(low[at]      , __lowerCAmelCase   )
				return out_edge_count

		for i in range(__lowerCAmelCase   ):
				if not visited[i]:
						_UpperCAmelCase		:       str								=      0
						_UpperCAmelCase		:       Tuple								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , -1      , __lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      out_edge_count > 1

		for x in range(len(__lowerCAmelCase   )   ):
				if is_art[x] is True:
						print(__lowerCAmelCase   )


# Adjacency list of graph
lowerCamelCase__    	=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)



