"""simple docstring"""


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


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     int   							=							'donut-swin'

    _snake_case   :     Union[str, Any]   							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(    self		:     int      , __lowerCAmelCase		:     Optional[Any]=224      , __lowerCAmelCase		:     str=4      , __lowerCAmelCase		:     Any=3      , __lowerCAmelCase		:     Tuple=96      , __lowerCAmelCase		:     List[Any]=[2, 2, 6, 2]      , __lowerCAmelCase		:     Optional[int]=[3, 6, 12, 24]      , __lowerCAmelCase		:     List[Any]=7      , __lowerCAmelCase		:     Tuple=4.0      , __lowerCAmelCase		:     List[str]=True      , __lowerCAmelCase		:     Tuple=0.0      , __lowerCAmelCase		:     Optional[Any]=0.0      , __lowerCAmelCase		:     Dict=0.1      , __lowerCAmelCase		:     int="gelu"      , __lowerCAmelCase		:     Tuple=False      , __lowerCAmelCase		:     int=0.02      , __lowerCAmelCase		:     Union[str, Any]=1e-5      , **__lowerCAmelCase		:     Optional[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					patch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					embed_dim
      _UpperCAmelCase         =					depths
      _UpperCAmelCase         =					len(__lowerCAmelCase    )
      _UpperCAmelCase         =					num_heads
      _UpperCAmelCase         =					window_size
      _UpperCAmelCase         =					mlp_ratio
      _UpperCAmelCase         =					qkv_bias
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					drop_path_rate
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					use_absolute_embeddings
      _UpperCAmelCase         =					layer_norm_eps
      _UpperCAmelCase         =					initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _UpperCAmelCase         =					int(embed_dim * 2 ** (len(__lowerCAmelCase    ) - 1)    )

"""simple docstring"""


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

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

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

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

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

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

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
