


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class    _a				(							_lowercase):
	_a    :    List[str]        =				['''image_processor''', '''tokenizer''']
	_a    :    Optional[int]        =				'''AutoImageProcessor'''
	_a    :    int        =				'''AutoTokenizer'''




	def __init__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						List[str]=None						,   _SCREAMING_SNAKE_CASE		:						int=None						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			int:
			lowerCAmelCase__  :     Dict       	=   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__  :     int       	=   kwargs.pop('''feature_extractor'''						)

			lowerCAmelCase__  :     List[Any]       	=   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						)
			lowerCAmelCase__  :     List[str]       	=   self.image_processor
			lowerCAmelCase__  :     List[str]       	=   False




	def __call__(   self		:						Dict						,   *_SCREAMING_SNAKE_CASE		:						str						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						)->			List[str]:
			# For backward compatibility
			if self._in_target_context_manager:
					return self.current_processor(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Dict       	=   kwargs.pop('''images'''						,   _SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Optional[Any]       	=   kwargs.pop('''text'''						,   _SCREAMING_SNAKE_CASE						)
			if len(_SCREAMING_SNAKE_CASE						) > 0:
					lowerCAmelCase__  :     List[Any]       	=   args[0]
					lowerCAmelCase__  :     Optional[int]       	=   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:
					lowerCAmelCase__  :     Optional[Any]       	=   self.image_processor(_SCREAMING_SNAKE_CASE						,   *_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)
			if text is not None:
					lowerCAmelCase__  :     str       	=   self.tokenizer(_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)

			if text is None:
					return inputs
			elif images is None:
					return encodings
			else:
					lowerCAmelCase__  :     Optional[int]       	=   encodings['''input_ids''']
					return inputs




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   *_SCREAMING_SNAKE_CASE		:						Optional[Any]						,   **_SCREAMING_SNAKE_CASE		:						Optional[int]						)->			List[str]:
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						str						,   *_SCREAMING_SNAKE_CASE		:						Dict						,   **_SCREAMING_SNAKE_CASE		:						Optional[int]						)->			List[str]:
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	@contextmanager
	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			Optional[Any]:
			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.'''						)
			lowerCAmelCase__  :     List[str]       	=   True
			lowerCAmelCase__  :     str       	=   self.tokenizer
			yield
			lowerCAmelCase__  :     Tuple       	=   self.image_processor
			lowerCAmelCase__  :     Any       	=   False




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








import math






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		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(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


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




