



import warnings

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




class 						__lowercase		(lowerCamelCase__							):




      """simple docstring"""





      _snake_case												=						["""image_processor""", """tokenizer"""]
      _snake_case												=						"""ViTImageProcessor"""
      _snake_case												=						("""CLIPTokenizer""", """CLIPTokenizerFast""")

      def __init__(				self    ,				A=None    ,				A=None    ,				**A     )		->    Dict:
         snake_case				:    Optional[int]           =	None
         if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""    ,				__snake_case    ,				)
            snake_case				:    List[str]           =	kwargs.pop("""feature_extractor"""     )

         snake_case				:    Tuple           =	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__(__snake_case    ,				__snake_case     )

      def __call__(				self    ,				A=None    ,				A=None    ,				A=None    ,				A=None    ,				**A     )		->    int:
         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:
            snake_case				:    Any           =	self.tokenizer(__snake_case    ,				return_tensors=__snake_case    ,				**__snake_case     )

         if visual_prompt is not None:
            snake_case				:    int           =	self.image_processor(__snake_case    ,				return_tensors=__snake_case    ,				**__snake_case     )

         if images is not None:
            snake_case				:    int           =	self.image_processor(__snake_case    ,				return_tensors=__snake_case    ,				**__snake_case     )

         if visual_prompt is not None and images is not None:
            snake_case				:    Optional[Any]           =	{
                '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:
            snake_case				:    List[Any]           =	image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         elif visual_prompt is not None:
            snake_case				:    Any           =	{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
         else:
            return BatchEncoding(data=dict(**__snake_case     )    ,				tensor_type=__snake_case     )

      def      UpperCAmelCase				(				self    ,				*A    ,				**A     )		->    List[Any]:
         return self.tokenizer.batch_decode(*__snake_case    ,				**__snake_case     )

      def      UpperCAmelCase				(				self    ,				*A    ,				**A     )		->    Dict:
         return self.tokenizer.decode(*__snake_case    ,				**__snake_case     )

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,				__snake_case    ,				)
         return self.image_processor_class

      @property
      def      UpperCAmelCase				(				self     )		->    Dict:
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,				__snake_case    ,				)
         return self.image_processor











# using dfs for finding eulerian path traversal




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None					)  ->       Any:
   snake_case				:    Union[str, Any]           =	(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         snake_case     , snake_case				:    int           =	True, True
         snake_case				:    List[Any]           =	dfs(lowercase						,lowercase						,lowercase						,lowercase					)
   return path




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	0
   snake_case				:    Union[str, Any]           =	-1
   for i in range(lowercase					):
      if i not in graph.keys():
         continue
      if len(graph[i]					) % 2 == 1:
         odd_degree_nodes += 1
         snake_case				:    str           =	i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	[[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
   snake_case     , snake_case				:    Any           =	check_circuit_or_path(lowercase						,lowercase					)
   if check == 3:
      print("""graph is not Eulerian"""					)
      print("""no path"""					)
      return
   snake_case				:    str           =	1
   if check == 2:
      snake_case				:    Optional[int]           =	odd_node
      print("""graph has a Euler path"""					)
   if check == 1:
      print("""graph has a Euler cycle"""					)
   snake_case				:    Dict           =	dfs(lowercase						,lowercase						,lowercase					)
   print(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   snake_case				:    List[Any]           =	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   snake_case				:    Tuple           =	{
       1: [],
       2: []
       # all degree is zero
   }
   snake_case				:    Tuple           =	10
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)


if __name__ == "__main__":
       main()





