


"""simple docstring"""






import heapq
def       A_   (   _lowerCAmelCase     :					dict       ):






     """simple docstring"""

     _a    	=				[]

     # for each node and his adjacency list add them and the rank of the node to queue
     # using heapq module the queue will be filled like a Priority Queue
     # heapq works with a min priority queue, so I used -1*len(v) to build it
     for key, value in graph.items():
          # O(log(n))
          heapq.heappush(_lowerCAmelCase,     [-1 * len(_lowerCAmelCase       ), (key, value)]       )

     # chosen_vertices = set of chosen vertices
     _a    	=				set()

     # while queue isn't empty and there are still edges
     #   (queue[0][0] is the rank of the node with max rank)
     while queue and queue[0][0] != 0:
          # extract vertex with max rank from queue and add it to chosen_vertices
          _a    	=				heapq.heappop(_lowerCAmelCase       )[1][0]
          chosen_vertices.add(_lowerCAmelCase       )

          # Remove all arcs adjacent to argmax
          for elem in queue:
               # if v haven't adjacent node, skip
               if elem[0] == 0:
                    continue
               # if argmax is reachable from elem
               # remove argmax from elem's adjacent list and update his rank
               if argmax in elem[1][1]:
                    _a    	=				elem[1][1].index(_lowerCAmelCase       )
                    del elem[1][1][index]
                    elem[0] += 1
        # re-order the queue
          heapq.heapify(_lowerCAmelCase       )
     return chosen_vertices


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __snake_case		=       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
   print(f'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')



"""simple docstring"""






import warnings

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


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['image_processor', 'tokenizer']
     A_       :	Union[str, Any]    =		'ChineseCLIPImageProcessor'
     A_       :	Union[str, Any]    =		('BertTokenizer', 'BertTokenizerFast')


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:
          _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.'''    ,				__UpperCAmelCase    ,				)
               _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__(__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				self.image_processor


     def __call__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:

          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    	=				self.tokenizer(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

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

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


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[int]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,				__UpperCAmelCase    ,				)
          return self.image_processor_class