





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   'vit_mae'


      def __init__(self	:				List[str]    ,      a__	:				Optional[int]=768    ,      a__	:				str=12    ,      a__	:				List[Any]=12    ,      a__	:				List[str]=3072    ,      a__	:				int="gelu"    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				List[str]=0.0    ,      a__	:				List[str]=0.0_2    ,      a__	:				Optional[int]=1E-12    ,      a__	:				Optional[Any]=224    ,      a__	:				int=16    ,      a__	:				str=3    ,      a__	:				int=True    ,      a__	:				Any=16    ,      a__	:				int=512    ,      a__	:				int=8    ,      a__	:				Dict=2048    ,      a__	:				str=0.7_5    ,      a__	:				Optional[Any]=False    ,      **a__	:				Union[str, Any]    ,      ):





         """simple docstring"""



         super().__init__(**a__				)

         __snake_case								=       hidden_size
         __snake_case								=       num_hidden_layers
         __snake_case								=       num_attention_heads
         __snake_case								=       intermediate_size
         __snake_case								=       hidden_act
         __snake_case								=       hidden_dropout_prob
         __snake_case								=       attention_probs_dropout_prob
         __snake_case								=       initializer_range
         __snake_case								=       layer_norm_eps
         __snake_case								=       image_size
         __snake_case								=       patch_size
         __snake_case								=       num_channels
         __snake_case								=       qkv_bias
         __snake_case								=       decoder_num_attention_heads
         __snake_case								=       decoder_hidden_size
         __snake_case								=       decoder_num_hidden_layers
         __snake_case								=       decoder_intermediate_size
         __snake_case								=       mask_ratio
         __snake_case								=       norm_pix_loss








import math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

