

'''simple docstring'''



import warnings

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







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      ["image_processor", "tokenizer"]
 lowerCAmelCase__  					=      "CLIPImageProcessor"
 lowerCAmelCase__  					=      ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=None					,							**__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""					,							__SCREAMING_SNAKE_CASE					,							)
           __SCREAMING_SNAKE_CASE     =      kwargs.pop("""feature_extractor"""			)

      __SCREAMING_SNAKE_CASE     =      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__(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def __call__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       str=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=None					,							**__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> str:


      """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:
           __SCREAMING_SNAKE_CASE     =      self.tokenizer(__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE     =      image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE			)					,							tensor_type=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]					,							*__SCREAMING_SNAKE_CASE					:       Dict					,							**__SCREAMING_SNAKE_CASE					:       int			)  -> Optional[Any]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int					,							*__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							**__SCREAMING_SNAKE_CASE					:       Any			)  -> Union[str, Any]:


      """simple docstring"""




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


 @property
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> str:


      """simple docstring"""




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









'''simple docstring'''



from __future__ import annotations







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     if len(a__						) == 0:
          raise ValueError("""find_max() arg is an empty sequence"""						)
     if (
         left >= len(a__						)
         or left < -len(a__						)
         or right >= len(a__						)
         or right < -len(a__						)
     ):
          raise IndexError("""list index out of range"""						)
     if left == right:
          return nums[left]
     __SCREAMING_SNAKE_CASE     =      (left + right) >> 1  # the middle
     __SCREAMING_SNAKE_CASE     =      find_max(a__      ,    a__      ,    a__						)  # find max in range[left, mid]
     __SCREAMING_SNAKE_CASE     =      find_max(a__      ,    mid + 1      ,    a__						)  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)






