




import warnings

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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		['image_processor', 'tokenizer']
 UpperCamelCase		     =		'CLIPImageProcessor'
 UpperCamelCase		     =		('CLIPTokenizer', 'CLIPTokenizerFast')

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

       """simple docstring"""






       lowerCamelCase_	  =					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__		,							)
             lowerCamelCase_	  =					kwargs.pop('feature_extractor'						)

       lowerCamelCase_	  =					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__						)

 def __call__(       self      :						Any		,							A_      :						Dict=None		,							A_      :						Union[str, Any]=None		,							A_      :						int=None		,							**A_      :						Union[str, Any]						)     ->			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:
             lowerCamelCase_	  =					self.tokenizer(UpperCAmelCase__		,							return_tensors=UpperCAmelCase__		,							**UpperCAmelCase__						)

       if images is not None:
             lowerCamelCase_	  =					self.image_processor(UpperCAmelCase__		,							return_tensors=UpperCAmelCase__		,							**UpperCAmelCase__						)

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

 def        a__				(       self      :						List[str]		,							*A_      :						Dict		,							**A_      :						int						)     ->			List[Any]:

       """simple docstring"""






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

 def        a__				(       self      :						Optional[int]		,							*A_      :						Optional[Any]		,							**A_      :						str						)     ->			Optional[Any]:

       """simple docstring"""






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

 @property
 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.tokenizer.model_input_names
       lowerCamelCase_	  =					self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)

 @property
 def        a__				(       self      :						Tuple						)     ->			Any:

       """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





 @property
 def        a__				(       self      :						Any						)     ->			str:

       """simple docstring"""






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





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		None
 UpperCamelCase		     =		None

lowerCamelCase       :	str									=     namedtuple("CoinsDistribResult", "moves excess")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: TreeNode | None  ):


      '''simple docstring'''



      if root is None:
            return 0

      # Validation
      def count_nodes(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowercase  ) != count_coins(lowercase  ):
            raise ValueError('The nodes number should be same as the number of coins'  )

      # Main calculation
      def get_distrib(lowercase			: TreeNode | None  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0			,							1  )

            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.left  )
            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.right  )

            lowerCamelCase_	  =					1 - left_distrib_excess
            lowerCamelCase_	  =					1 - right_distrib_excess

            lowerCamelCase_	  =					(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowercase  )
                + abs(lowercase  )
            )
            lowerCamelCase_	  =					node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowercase			,							lowercase  )

      return get_distrib(lowercase  )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



