




import warnings

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







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Any						=     ["image_processor", "tokenizer"]
				__a     :       Tuple						=     "ChineseCLIPImageProcessor"
				__a     :       List[Any]						=     ("BertTokenizer", "BertTokenizerFast")






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      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__      ,)
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      kwargs.pop('feature_extractor' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      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__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.image_processor






				def __call__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):

							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_   : List[Any]					  =      self.tokenizer(snake_case__      ,return_tensors=snake_case__      ,**snake_case__ )

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

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






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.batch_decode(*snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.decode(*snake_case__      ,**snake_case__ )






				@property
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.tokenizer.model_input_names
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






				@property
				def 		snake_case	(			self ):
							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









def        __UpperCAmelCase						(							lowerCamelCase_ :     str  )       ->  bool:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      0
			for ch in input_str:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ord(lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Tuple					  =      pow(2  ,							lowerCamelCase_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()



