'''simple docstring'''






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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    '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				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""donut-swin"""

  __lowerCAmelCase				      =					{
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=4   ,    snake_case_=3   ,    snake_case_=96   ,    snake_case_=[2, 2, 6, 2]   ,    snake_case_=[3, 6, 12, 24]   ,    snake_case_=7   ,    snake_case_=4.0   ,    snake_case_=True   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.1   ,    snake_case_="gelu"   ,    snake_case_=False   ,    snake_case_=0.0_2   ,    snake_case_=1e-5   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					str		= patch_size
    __UpperCAmelCase:					int		= num_channels
    __UpperCAmelCase:					List[Any]		= embed_dim
    __UpperCAmelCase:					Tuple		= depths
    __UpperCAmelCase:					str		= len(snake_case_					)
    __UpperCAmelCase:					str		= num_heads
    __UpperCAmelCase:					Union[str, Any]		= window_size
    __UpperCAmelCase:					Dict		= mlp_ratio
    __UpperCAmelCase:					Any		= qkv_bias
    __UpperCAmelCase:					int		= hidden_dropout_prob
    __UpperCAmelCase:					Tuple		= attention_probs_dropout_prob
    __UpperCAmelCase:					List[Any]		= drop_path_rate
    __UpperCAmelCase:					str		= hidden_act
    __UpperCAmelCase:					List[str]		= use_absolute_embeddings
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					Union[str, Any]		= 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:					List[Any]		= int(embed_dim * 2 ** (len(snake_case_					) - 1)					)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)