






"""simple docstring"""







class   UpperCamelCase_ :  # Public class to implement a graph



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			row
         UpperCAmelCase	    =			col
         UpperCAmelCase	    =			graph



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         return (
             0 <= i < self.ROW
             and 0 <= j < self.COL
             and not visited[i][j]
             and self.graph[i][j]
         )



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
         UpperCAmelCase	    =			[-1, 0, 1, -1, 1, -1, 0, 1]
         UpperCAmelCase	    =			True  # Make those cells visited
         for k in range(8				):
                if self.is_safe(i + row_nbr[k] ,		j + col_nbr[k] ,		A__				):
                       self.diffs(i + row_nbr[k] ,		j + col_nbr[k] ,		A__				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:  # And finally, count all islands.



         """simple docstring"""





         UpperCAmelCase	    =			[[False for j in range(self.COL				)] for i in range(self.ROW				)]
         UpperCAmelCase	    =			0
         for i in range(self.ROW				):
                for j in range(self.COL				):
                       if visited[i][j] is False and self.graph[i][j] == 1:
                              self.diffs(A__ ,		A__ ,		A__				)
                              count += 1
         return count











"""simple docstring"""







import warnings

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





class   UpperCamelCase_ (       a_  ):
  _A       :					Optional[int]				=	['image_processor', 'tokenizer']
  _A       :					Optional[Any]				=	'ViTImageProcessor'
  _A       :					Tuple				=	('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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__ ,		)
                UpperCAmelCase	    =			kwargs.pop("""feature_extractor"""				)

         UpperCAmelCase	    =			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 ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			List[str]:



         """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:
                UpperCAmelCase	    =			self.tokenizer(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if images is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

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



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





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



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         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				)			->			Any:



         """simple docstring"""





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