



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    ["""image_processor""", """tokenizer"""]
	lowercase__						       =    """AutoImageProcessor"""
	lowercase__						       =    """AutoTokenizer"""





	def __init__(				self      :       int				,lowerCamelCase__      :       List[Any]=None				,lowerCamelCase__      :       Tuple=None				,**lowerCamelCase__      :       Tuple				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'				,__lowerCamelCase				,)
													_UpperCamelCase      :	Dict				       =     kwargs.pop('feature_extractor'				)

							_UpperCamelCase      :	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__(__lowerCamelCase				,__lowerCamelCase				)
							_UpperCamelCase      :	Tuple				       =     self.image_processor
							_UpperCamelCase      :	Dict				       =     False





	def __call__(				self      :       Dict				,*lowerCamelCase__      :       Optional[Any]				,**lowerCamelCase__      :       Tuple				):







							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
													return self.current_processor(*__lowerCamelCase				,**__lowerCamelCase				)

							_UpperCamelCase      :	Optional[Any]				       =     kwargs.pop('images'				,__lowerCamelCase				)
							_UpperCamelCase      :	Optional[Any]				       =     kwargs.pop('text'				,__lowerCamelCase				)
							if len(__lowerCamelCase				) > 0:
													_UpperCamelCase      :	Dict				       =     args[0]
													_UpperCamelCase      :	str				       =     args[1:]

							if images is None and text is None:
													raise ValueError('You need to specify either an `images` or `text` input to process.'				)

							if images is not None:
													_UpperCamelCase      :	Union[str, Any]				       =     self.image_processor(__lowerCamelCase				,*__lowerCamelCase				,**__lowerCamelCase				)
							if text is not None:
													_UpperCamelCase      :	str				       =     self.tokenizer(__lowerCamelCase				,**__lowerCamelCase				)

							if text is None:
													return inputs
							elif images is None:
													return encodings
							else:
													_UpperCamelCase      :	List[Any]				       =     encodings["input_ids"]
													return inputs





	def 			UpperCamelCase_						(				self      :       str				,*lowerCamelCase__      :       int				,**lowerCamelCase__      :       int				):







							'''simple docstring'''
							return self.tokenizer.batch_decode(*__lowerCamelCase				,**__lowerCamelCase				)





	def 			UpperCamelCase_						(				self      :       Optional[int]				,*lowerCamelCase__      :       Optional[int]				,**lowerCamelCase__      :       int				):







							'''simple docstring'''
							return self.tokenizer.decode(*__lowerCamelCase				,**__lowerCamelCase				)





	@contextmanager
	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your images inputs, or in a separate call.'				)
							_UpperCamelCase      :	List[str]				       =     True
							_UpperCamelCase      :	Union[str, Any]				       =     self.tokenizer
							yield
							_UpperCamelCase      :	List[Any]				       =     self.image_processor
							_UpperCamelCase      :	Optional[Any]				       =     False





	@property
	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,__lowerCamelCase				,)
							return self.image_processor_class





	@property
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,__lowerCamelCase				,)
							return self.image_processor







'''simple docstring'''
import math






def A__					(  UpperCAmelCase_							):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5						,						int(math.sqrt(UpperCAmelCase_							) + 1							)						,						6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True






def A__					(  UpperCAmelCase_ = 1_0_0_0						,						UpperCAmelCase_ = 1_0_0_0							):
						_UpperCamelCase      :	str				       =     [0, 0, 0]  # length, a, b
						for a in range((a_limit * -1) + 1						,						UpperCAmelCase_							):
												for b in range(2						,						UpperCAmelCase_							):
																		if is_prime(UpperCAmelCase_							):
																								_UpperCamelCase      :	List[str]				       =     0
																								_UpperCamelCase      :	Union[str, Any]				       =     0
																								while is_prime((n**2) + (a * n) + b							):
																														count += 1
																														n += 1
																								if count > longest[0]:
																														_UpperCamelCase      :	Any				       =     [count, a, b]
						_UpperCamelCase      :	Dict				       =     longest[1] * longest[2]
						return ans


if __name__ == "__main__":
					print(solution(1000, 1000))


