

'''simple docstring'''
import warnings

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


class    a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     int	       =							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE    :     Optional[Any]	       =							'ChineseCLIPImageProcessor'
   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							('BertTokenizer', 'BertTokenizerFast')


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





      """simple docstring"""





      _lowercase			:      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			,				)
         _lowercase			:      Any			     =   kwargs.pop("feature_extractor"  )

      _lowercase			:      Union[str, Any]			     =   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  )
      _lowercase			:      Optional[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:
         _lowercase			:      int			     =   self.tokenizer(_UpperCamelCase			,				return_tensors=_UpperCamelCase			,				**_UpperCamelCase  )

      if images is not None:
         _lowercase			:      Tuple			     =   self.image_processor(_UpperCamelCase			,				return_tensors=_UpperCamelCase			,				**_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase			:      Dict			     =   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"""





      _lowercase			:      str			     =   self.tokenizer.model_input_names
      _lowercase			:      Optional[int]			     =   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









from __future__ import annotations
def      a      (			A__	:   list[int]     )    ->      int:





      """simple docstring"""



      if not nums:
            return 0
      _lowercase	       =nums[0]
      _lowercase	       =0
      for num in nums[1:]:
            _lowercase       ,       _lowercase	       =(
                max_excluding + num,
                max(A__      ,   A__     ),
            )
      return max(A__      ,   A__     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


