






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


class        __UpperCamelCase    (     _UpperCAmelCase   ):
	__snake_case	:Any   =       ['image_processor', 'tokenizer']
	__snake_case	:List[str]   =       'AutoImageProcessor'
	__snake_case	:Dict   =       'AutoTokenizer'







	def __init__(  self      :							Optional[int]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Any			)		-> List[str]:
		"""simple docstring"""







		super().__init__(__UpperCamelCase       ,				__UpperCamelCase			)
		__lowercase             =  self.image_processor







	def __call__(  self      :							int       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							str=None       ,				_lowerCAmelCase      :							Tuple=None       ,				**_lowerCAmelCase      :							str			)		-> Optional[Any]:
		"""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(__UpperCamelCase       ,				return_tensors=__UpperCamelCase       ,				**__UpperCamelCase			)

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

		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(**__UpperCamelCase			)       ,				tensor_type=__UpperCamelCase			)







	def  _a   (  self      :							Optional[Any]       ,				*_lowerCAmelCase      :							Optional[Any]       ,				**_lowerCAmelCase      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[int]       ,				*_lowerCAmelCase      :							Dict       ,				**_lowerCAmelCase      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







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







	@property
	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		return ["input_ids", "attention_mask", "pixel_values"]














def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if not isinstance(lowerCamelCase				,  lowerCamelCase					):
		raise ValueError("""check_bouncy() accepts only integer arguments"""					)
	__lowercase             =  str(lowerCamelCase					)
	__lowercase             =  """""".join(sorted(lowerCamelCase					)					)
	return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				snake_case  (     lowerCamelCase = 99					):




	'''simple docstring'''







	if not 0 < percent < 100:
		raise ValueError("""solution() only accepts values from 0 to 100"""					)
	__lowercase             =  0
	__lowercase             =  1

	while True:
		if check_bouncy(lowerCamelCase					):
			bouncy_num += 1
		if (bouncy_num / num) * 100 >= percent:
			return num
		num += 1


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						print(F'''{solution(99)}''')

