



import warnings

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


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    int											=					["""image_processor""", """tokenizer"""]
		__lowerCAmelCase	:    int											=					"""ViTImageProcessor"""
		__lowerCAmelCase	:    int											=					("""CLIPTokenizer""", """CLIPTokenizerFast""")



		def __init__(     self     :Optional[Any]   ,__lowercase     :int=None   ,__lowercase     :Union[str, Any]=None   ,**__lowercase     :str  ):
						snake_case__	:      List[str]     		=	None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
										    ''' instead.'''   ,__lowercase   ,)
										snake_case__	:      Union[str, Any]     		=	kwargs.pop('''feature_extractor'''  )

						snake_case__	:      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__(__lowercase   ,__lowercase  )



		def __call__(     self     :List[Any]   ,__lowercase     :List[str]=None   ,__lowercase     :Tuple=None   ,__lowercase     :Union[str, Any]=None   ,__lowercase     :Optional[Any]=None   ,**__lowercase     :Tuple  ):
						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:
										snake_case__	:      List[Any]     		=	self.tokenizer(__lowercase   ,return_tensors=__lowercase   ,**__lowercase  )

						if visual_prompt is not None:
										snake_case__	:      List[Any]     		=	self.image_processor(__lowercase   ,return_tensors=__lowercase   ,**__lowercase  )

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

						if visual_prompt is not None and images is not None:
										snake_case__	:      List[Any]     		=	{
										    '''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:
										snake_case__	:      Union[str, Any]     		=	image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						elif visual_prompt is not None:
										snake_case__	:      Tuple     		=	{
										    '''conditional_pixel_values''': prompt_features.pixel_values,
										}
										return encoding
						else:
										return BatchEncoding(data=dict(**__lowercase  )   ,tensor_type=__lowercase  )



		def 	__lowerCamelCase			(     self     :Any   ,*__lowercase     :int   ,**__lowercase     :Union[str, Any]  ):
						return self.tokenizer.batch_decode(*__lowercase   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :int   ,*__lowercase     :List[Any]   ,**__lowercase     :Dict  ):
						return self.tokenizer.decode(*__lowercase   ,**__lowercase  )



		@property
		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						warnings.warn(
						    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,__lowercase   ,)
						return self.image_processor_class






		@property
		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						warnings.warn(
						    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,__lowercase   ,)
						return self.image_processor






import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case__	:      List[str]     		=	0
				while count < prec:
								snake_case__	:      List[str]     		=	random.randint(2  ,  n - 1      )
								snake_case__	:      Optional[Any]     		=	bin_exp_mod(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
								if b != 1:
												snake_case__	:      List[Any]     		=	True
												for _ in range(__lowerCAmelCase      ):
																if b == n - 1:
																				snake_case__	:      List[str]     		=	False
																				break
																snake_case__	:      str     		=	b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
				A__								=						abs(int(input('''Enter bound : ''').strip()))
				print('''Here\'s the list of primes:''')
				print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))

