

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    '''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  __lowerCAmelCase     (					__snake_case							):
  _a									=  """levit"""




  def __init__(				self							,   lowerCAmelCase=224							,   lowerCAmelCase=3							,   lowerCAmelCase=3							,   lowerCAmelCase=2							,   lowerCAmelCase=1							,   lowerCAmelCase=16							,   lowerCAmelCase=[128, 256, 384]							,   lowerCAmelCase=[4, 8, 12]							,   lowerCAmelCase=[4, 4, 4]							,   lowerCAmelCase=[16, 16, 16]							,   lowerCAmelCase=0							,   lowerCAmelCase=[2, 2, 2]							,   lowerCAmelCase=[2, 2, 2]							,   lowerCAmelCase=0.02							,   **lowerCAmelCase							,   )  -> Optional[int]:
        '''simple docstring'''






        super().__init__(**__SCREAMING_SNAKE_CASE					)
        _lowercase	       =image_size
        _lowercase	       =num_channels
        _lowercase	       =kernel_size
        _lowercase	       =stride
        _lowercase	       =padding
        _lowercase	       =hidden_sizes
        _lowercase	       =num_attention_heads
        _lowercase	       =depths
        _lowercase	       =key_dim
        _lowercase	       =drop_path_rate
        _lowercase	       =patch_size
        _lowercase	       =attention_ratio
        _lowercase	       =mlp_ratio
        _lowercase	       =initializer_range
        _lowercase	       =[
            ['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  __lowerCAmelCase     (					__snake_case							):
  _a									=  version.parse("""1.11"""							)




  @property
  def   A__     (				self					)  -> Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






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




  @property
  def   A__     (				self					)  -> float:
        '''simple docstring'''






        return 1e-4










"""simple docstring"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


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




