
import warnings

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

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Any        =		["""image_processor""", """tokenizer"""]
		a__       :						str        =		"""ViTImageProcessor"""
		a__       :						str        =		("""CLIPTokenizer""", """CLIPTokenizerFast""")






		def __init__(		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=None							,			**SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							List[str]:
						__lowerCamelCase    					=	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__							,			)
										__lowerCamelCase    					=	kwargs.pop('''feature_extractor'''  )

						__lowerCamelCase    					=	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		:							str							,			SCREAMING_SNAKE_CASE__		:							int=None							,			SCREAMING_SNAKE_CASE__		:							List[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Tuple=None							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Optional[int]:
						if text is None and visual_prompt is None and images is None:
										raise ValueError('''You have to specify either text, visual prompt or images.'''  )

						if text is not None and visual_prompt is not None:
										raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

						if text is not None:
										__lowerCamelCase    					=	self.tokenizer(SCREAMING_SNAKE_CASE__							,			return_tensors=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

						if visual_prompt is not None:
										__lowerCamelCase    					=	self.image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

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

						if visual_prompt is not None and images is not None:
										__lowerCamelCase    					=	{
										    '''pixel_values''': image_features.pixel_values,
										    '''conditional_pixel_values''': prompt_features.pixel_values,
										}
										return encoding
						elif text is not None and images is not None:
										__lowerCamelCase    					=	image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						elif visual_prompt is not None:
										__lowerCamelCase    					=	{
										    '''conditional_pixel_values''': prompt_features.pixel_values,
										}
										return encoding
						else:
										return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__  )							,			tensor_type=SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str							,			*SCREAMING_SNAKE_CASE__		:							Any							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			*SCREAMING_SNAKE_CASE__		:							int							,			**SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Any:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		@property
		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						warnings.warn(
						    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,			SCREAMING_SNAKE_CASE__							,			)
						return self.image_processor_class







		@property
		def  __A       (		self		:							List[Any]  )     ->							Any:
						warnings.warn(
						    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,			SCREAMING_SNAKE_CASE__							,			)
						return self.image_processor





from __future__ import annotations

from fractions import Fraction




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						int							)			->   bool:
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   list[str]:
				__lowerCamelCase    					=	[]
				__lowerCamelCase    					=	11
				__lowerCamelCase    					=	int('''1''' + '''0''' * digit_len							)
				for num in range(__lowerCAmelCase		,  __lowerCAmelCase							):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(__lowerCAmelCase		,  __lowerCAmelCase							):
																				solutions.append(f'''{num}/{den}'''							)
												den += 1
								num += 1
								__lowerCamelCase    					=	10
				return solutions




def  __magic_name__ (   __lowerCAmelCase	:						int = 2							)			->   int:
				__lowerCamelCase    					=	1.0
				for fraction in fraction_list(__lowerCAmelCase							):
								__lowerCamelCase    					=	Fraction(__lowerCAmelCase							)
								result *= frac.denominator / frac.numerator
				return int(__lowerCAmelCase							)


if __name__ == "__main__":
					print(solution())



