


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


UpperCamelCase					:       Dict			    =     logging.get_logger(__name__)

UpperCamelCase					:       Tuple			    =     {
    """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__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'donut-swin'

  _lowercase									=		{
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }

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

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





"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase							       =     True
						for i in range(0       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False

						for i in range(1       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A	:  Tuple            =     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A	:  Dict            =     odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




