


"""simple docstring"""



def     __lowerCAmelCase		(		lowercase    :    int					)      ->		Dict:


    """simple docstring"""





    snake_case   ,snake_case       : Union[str, Any]    =       [], []
    while len(lowercase					) > 1:
        snake_case   ,snake_case       : List[str]    =       min(lowercase					), max(lowercase					)
        start.append(lowercase					)
        end.append(lowercase					)
        collection.remove(lowercase					)
        collection.remove(lowercase					)
    end.reverse()
    return start + collection + end


if __name__ == "__main__":
  __snake_case       =       input("""Enter numbers separated by a comma:\n""").strip()
  __snake_case       =       [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")









"""simple docstring"""



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__snake_case       =       {
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Any				    =    '''maskformer'''
   __UpperCAmelCase					:			List[str]				    =    {'''hidden_size''': '''mask_feature_size'''}
   __UpperCAmelCase					:			Optional[Any]				    =    ['''resnet''', '''swin''']
   __UpperCAmelCase					:			str				    =    ['''detr''']







   def __init__(      self		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 20.0		,      UpperCamelCase__ = None		,      **UpperCamelCase__		,      )      ->						Optional[int]:

       '''simple docstring'''




       if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           snake_case       : Tuple    =       SwinConfig(
               image_size=384		,      in_channels=3		,      patch_size=4		,      embed_dim=128		,      depths=[2, 2, 18, 2]		,      num_heads=[4, 8, 16, 32]		,      window_size=12		,      drop_path_rate=0.3		,      out_features=["stage1", "stage2", "stage3", "stage4"]		,      )

       if isinstance(UpperCamelCase__		,      UpperCamelCase__		):
           snake_case       : Optional[Any]    =       backbone_config.pop("model_type"		)
           snake_case       : List[str]    =       CONFIG_MAPPING[backbone_model_type]
           snake_case       : str    =       config_class.from_dict(UpperCamelCase__		)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported		)}'		)

       if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           snake_case       : Optional[int]    =       DetrConfig()
       else:
           # verify that the decoder is supported
           snake_case       : Optional[Any]    =       (
               decoder_config.pop("model_type"		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
               raise ValueError(
                   F'Transformer Decoder {decoder_type} not supported, please use one of'
                   F' {",".join(self.decoders_supported		)}'		)
           if isinstance(UpperCamelCase__		,      UpperCamelCase__		):
               snake_case       : Dict    =       CONFIG_MAPPING[decoder_type]
               snake_case       : List[Any]    =       config_class.from_dict(UpperCamelCase__		)

       snake_case       : Optional[int]    =       backbone_config
       snake_case       : Optional[Any]    =       decoder_config
       # main feature dimension for the model
       snake_case       : List[str]    =       fpn_feature_size
       snake_case       : Tuple    =       mask_feature_size
       # initializer
       snake_case       : str    =       init_std
       snake_case       : str    =       init_xavier_std
       # Hungarian matcher && loss
       snake_case       : List[Any]    =       cross_entropy_weight
       snake_case       : int    =       dice_weight
       snake_case       : str    =       mask_weight
       snake_case       : Any    =       use_auxiliary_loss
       snake_case       : Any    =       no_object_weight
       snake_case       : List[Any]    =       output_auxiliary_logits

       snake_case       : Optional[int]    =       self.decoder_config.encoder_attention_heads
       snake_case       : Tuple    =       self.decoder_config.num_hidden_layers
       super().__init__(**UpperCamelCase__		)







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       return cls(
           backbone_config=UpperCamelCase__		,      decoder_config=UpperCamelCase__		,      **UpperCamelCase__		,      )







   def 					lowerCamelCase						(      self		)      ->						Dict[str, any]:

       '''simple docstring'''




       snake_case       : int    =       copy.deepcopy(self.__dict__		)
       snake_case       : List[str]    =       self.backbone_config.to_dict()
       snake_case       : Union[str, Any]    =       self.decoder_config.to_dict()
       snake_case       : Optional[int]    =       self.__class__.model_type
       return output





