def 		snake_case__	(		lowercase					):  # noqa: E741
   lowerCAmelCase_:							str       =	len(__a					)
   lowerCAmelCase_:							Optional[Any]       =	0
   lowerCAmelCase_:							Dict       =	[0] * n
   lowerCAmelCase_:							Optional[Any]       =	[False] * n
   lowerCAmelCase_:							List[str]       =	[False] * n

   def dfs(lowercase							,				lowercase							,				lowercase							,				lowercase					):
      if parent == root:
         out_edge_count += 1
      lowerCAmelCase_:							Optional[int]       =	True
      lowerCAmelCase_:							Any       =	at

      for to in l[at]:
         if to == parent:
            pass
         elif not visited[to]:
            lowerCAmelCase_:							List[Any]       =	dfs(__a							,				__a							,				__a							,				__a					)
            lowerCAmelCase_:							List[str]       =	min(low[at]							,				low[to]					)

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

   for i in range(__a					):
      if not visited[i]:
         lowerCAmelCase_:							Union[str, Any]       =	0
         lowerCAmelCase_:							Any       =	dfs(__a							,				__a							,				-1							,				__a					)
         lowerCAmelCase_:							List[Any]       =	out_edge_count > 1

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


# Adjacency list of graph
a  :      Optional[Any]            =    {
    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)



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''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.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''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__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

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

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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