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


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)

__lowerCamelCase      :			Any							       =		{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_   							=							"levit"




   def __init__(   self      :    str	,     _lowercase      :    Optional[int]=2_24	,     _lowercase      :    str=3	,     _lowercase      :    Optional[Any]=3	,     _lowercase      :    List[Any]=2	,     _lowercase      :    Dict=1	,     _lowercase      :    Tuple=16	,     _lowercase      :    List[Any]=[1_28, 2_56, 3_84]	,     _lowercase      :    Optional[Any]=[4, 8, 12]	,     _lowercase      :    List[Any]=[4, 4, 4]	,     _lowercase      :    Dict=[16, 16, 16]	,     _lowercase      :    Union[str, Any]=0	,     _lowercase      :    List[str]=[2, 2, 2]	,     _lowercase      :    Optional[int]=[2, 2, 2]	,     _lowercase      :    int=0.02	,     **_lowercase      :    Dict	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				image_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				kernel_size
     SCREAMING_SNAKE_CASE__      							=				stride
     SCREAMING_SNAKE_CASE__      							=				padding
     SCREAMING_SNAKE_CASE__      							=				hidden_sizes
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				depths
     SCREAMING_SNAKE_CASE__      							=				key_dim
     SCREAMING_SNAKE_CASE__      							=				drop_path_rate
     SCREAMING_SNAKE_CASE__      							=				patch_size
     SCREAMING_SNAKE_CASE__      							=				attention_ratio
     SCREAMING_SNAKE_CASE__      							=				mlp_ratio
     SCREAMING_SNAKE_CASE__      							=				initializer_range
     SCREAMING_SNAKE_CASE__      							=				[
         ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_   							=							version.parse("1.11"		)




   @property
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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




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



     """simple docstring"""






     return 1E-4

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[True] * max_number
  for i in range(2			,					isqrt(max_number - 1     ) + 1     ):
    if is_prime[i]:
      for j in range(i**2			,					__UpperCamelCase			,					__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__      							=				False

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


if __name__ == "__main__":
   print(F"""{solution() = }""")
