




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


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'google/vit-base-patch16-224': 'https://huggingface.co/vit-base-patch16-224/resolve/main/config.json',
    # See all ViT models at https://huggingface.co/models?filter=vit
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Tuple							=   'vit'


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





         """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								=       encoder_stride






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   version.parse('1.11'  )


      @property
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



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


      @property
      def        a			(self	:				int				):





         """simple docstring"""



         return 1E-4








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))

