




"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_						=				logging.get_logger(__name__)

A_						=				{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"data2vec-vision"


 def __init__(				self:		Optional[Any],      a_:		Tuple=768,      a_:		int=12,      a_:		Any=12,      a_:		Union[str, Any]=3_072,      a_:		Union[str, Any]="gelu",      a_:		Union[str, Any]=0.0,      a_:		str=0.0,      a_:		int=0.02,      a_:		Optional[int]=1E-12,      a_:		Dict=224,      a_:		Optional[int]=16,      a_:		Any=3,      a_:		List[Any]=False,      a_:		int=False,      a_:		Dict=False,      a_:		Union[str, Any]=False,      a_:		List[str]=0.1,      a_:		int=0.1,      a_:		Optional[int]=True,      a_:		List[Any]=[3, 5, 7, 11],      a_:		List[Any]=[1, 2, 3, 6],      a_:		int=True,      a_:		int=0.4,      a_:		int=256,      a_:		Optional[Any]=1,      a_:		Tuple=False,      a_:		str=255,      **a_:		Dict,      ):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Optional[int]								=  hidden_size
   _snake_case							:		List[Any]								=  num_hidden_layers
   _snake_case							:		Union[str, Any]								=  num_attention_heads
   _snake_case							:		Any								=  intermediate_size
   _snake_case							:		int								=  hidden_act
   _snake_case							:		Optional[Any]								=  hidden_dropout_prob
   _snake_case							:		List[str]								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  initializer_range
   _snake_case							:		Tuple								=  layer_norm_eps

   _snake_case							:		Tuple								=  image_size
   _snake_case							:		Optional[int]								=  patch_size
   _snake_case							:		Optional[Any]								=  num_channels
   _snake_case							:		Any								=  use_mask_token
   _snake_case							:		str								=  use_absolute_position_embeddings
   _snake_case							:		Optional[int]								=  use_relative_position_bias
   _snake_case							:		List[str]								=  use_shared_relative_position_bias
   _snake_case							:		Tuple								=  layer_scale_init_value
   _snake_case							:		Optional[Any]								=  drop_path_rate
   _snake_case							:		Union[str, Any]								=  use_mean_pooling
   # decode head attributes (semantic segmentation)
   _snake_case							:		List[Any]								=  out_indices
   _snake_case							:		List[str]								=  pool_scales
   # auxiliary head attributes (semantic segmentation)
   _snake_case							:		Tuple								=  use_auxiliary_head
   _snake_case							:		List[str]								=  auxiliary_loss_weight
   _snake_case							:		List[str]								=  auxiliary_channels
   _snake_case							:		List[Any]								=  auxiliary_num_convs
   _snake_case							:		List[str]								=  auxiliary_concat_input
   _snake_case							:		List[str]								=  semantic_loss_ignore_index







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					version.parse("1.11" )


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




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]		)


 @property
 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   return 1E-4







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)))

