





'''simple docstring'''






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  Dict      ,a_						:  List[Any]	):

		if height >= 1:
				move_tower(height - 1      ,snake_case__      ,snake_case__      ,snake_case__	)
				move_disk(snake_case__      ,snake_case__	)
				move_tower(height - 1      ,snake_case__      ,snake_case__      ,snake_case__	)






def      __A						(							a_						:  Dict      ,a_						:  Union[str, Any]	):
		print("moving disk from"      ,snake_case__      ,"to"      ,snake_case__	)






def      __A						(							):
		lowerCAmelCase     :    Any						=   int(input("Height of hanoi: "	).strip()	)
		move_tower(snake_case__      ,"A"      ,"B"      ,"C"	)


if __name__ == "__main__":
					main()










'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "ChineseCLIPImageProcessor"
	snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



	def __init__(   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    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."		,      a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   kwargs.pop("feature_extractor"	)

			lowerCAmelCase     :    int						=   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__(a_		,      a_	)
			lowerCAmelCase     :    Dict						=   self.image_processor



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

			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:
					lowerCAmelCase     :    List[str]						=   self.tokenizer(a_		,      return_tensors=a_		,      **a_	)

			if images is not None:
					lowerCAmelCase     :    List[Any]						=   self.image_processor(a_		,      return_tensors=a_		,      **a_	)

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



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*a_		,      **a_	)



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.tokenizer.model_input_names
			lowerCAmelCase     :    List[Any]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def   _lowerCamelCase    (   self	):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,      a_		,      )
			return self.image_processor_class



