




'''simple docstring'''





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  _snake_case		(	lowerCAmelCase__							):
			lowerCAmelCase_     :      int						       =    ["""image_processor""", """tokenizer"""]
			lowerCAmelCase_     :      Tuple						       =    """BridgeTowerImageProcessor"""
			lowerCAmelCase_     :      List[str]						       =    ("""RobertaTokenizer""", """RobertaTokenizerFast""")




			def __init__(					self				,							a__				,							a__							)				->				str:

										'''simple docstring'''




										super().__init__(UpperCamelCase__				,							UpperCamelCase__							)




			def __call__(					self				,							a__				,							a__ = None				,							a__ = True				,							a__ = False				,							a__ = None				,							a__ = None				,							a__ = 0				,							a__ = None				,							a__ = None				,							a__ = None				,							a__ = False				,							a__ = False				,							a__ = False				,							a__ = False				,							a__ = True				,							a__ = None				,							**a__				,							)				->				BatchEncoding:

										'''simple docstring'''




										snake_case_						=      self.tokenizer(
										    text=UpperCamelCase__				,							add_special_tokens=UpperCamelCase__				,							padding=UpperCamelCase__				,							truncation=UpperCamelCase__				,							max_length=UpperCamelCase__				,							stride=UpperCamelCase__				,							pad_to_multiple_of=UpperCamelCase__				,							return_token_type_ids=UpperCamelCase__				,							return_attention_mask=UpperCamelCase__				,							return_overflowing_tokens=UpperCamelCase__				,							return_special_tokens_mask=UpperCamelCase__				,							return_offsets_mapping=UpperCamelCase__				,							return_length=UpperCamelCase__				,							verbose=UpperCamelCase__				,							return_tensors=UpperCamelCase__				,							**UpperCamelCase__				,							)
										# add pixel_values + pixel_mask
										snake_case_						=      self.image_processor(
										    UpperCamelCase__				,							return_tensors=UpperCamelCase__				,							do_normalize=UpperCamelCase__				,							do_center_crop=UpperCamelCase__				,							**UpperCamelCase__							)
										encoding.update(UpperCamelCase__							)

										return encoding




			def    lowerCAmelCase__				(					self				,							*a__				,							**a__							)				->				int:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							*a__				,							**a__							)				->				Optional[Any]:

										'''simple docstring'''




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




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      self.tokenizer.model_input_names
										snake_case_						=      self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)










import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

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

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   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)))






