
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 	A__   (						lowerCAmelCase__				):
							lowerCAmelCase__		:						Any    			=							["image_processor", "tokenizer"]
							lowerCAmelCase__		:						int    			=							"CLIPImageProcessor"
							lowerCAmelCase__		:						Tuple    			=							("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")





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



											"""simple docstring"""


											__lowercase								=					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     , )
															__lowercase								=					kwargs.pop('feature_extractor'				)

											__lowercase								=					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]     , _UpperCAmelCase  :   int=None     , _UpperCAmelCase  :   str=None     , _UpperCAmelCase  :   List[Any]=None     , **_UpperCAmelCase  :   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:
															__lowercase								=					self.tokenizer(__SCREAMING_SNAKE_CASE     , return_tensors=__SCREAMING_SNAKE_CASE     , **__SCREAMING_SNAKE_CASE				)

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

											if text is not None and images is not None:
															__lowercase								=					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 			a__ ( self  :   List[str]     , *_UpperCAmelCase  :   Dict     , **_UpperCAmelCase  :   int				)						->		Optional[Any]:



											"""simple docstring"""


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





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



											"""simple docstring"""


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





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



											"""simple docstring"""


											__lowercase								=					self.tokenizer.model_input_names
											__lowercase								=					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)






