
from __future__ import annotations

import math
import random



def      __UpperCAmelCase    (  snake_case_							:		int				,					snake_case_							:		int				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     d * e - 1
	UpperCAmelCase:					Tuple 						=     0
	UpperCAmelCase:					Union[str, Any] 						=     0
	while p == 0:
		UpperCAmelCase:					List[Any] 						=     random.randint(2				,					n - 1				)
		UpperCAmelCase:					Optional[int] 						=     k
		while True:
			if t % 2 == 0:
				UpperCAmelCase:					Dict 						=     t // 2
				UpperCAmelCase:					str 						=     (g**t) % n
				UpperCAmelCase:					Any 						=     math.gcd(x - 1				,					snake_case_				)
				if x > 1 and y > 1:
					UpperCAmelCase:					Tuple 						=     y
					UpperCAmelCase:					Optional[Any] 						=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


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





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     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:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     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     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



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



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



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




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
