






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase			:					str         =	logging.get_logger(__name__)

lowerCamelCase			:					List[str]         =	{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""deta"""
      snake_case_  =							{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }


      def __init__(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=9_0_0					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=6					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=8					,						__UpperCamelCase=6					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=8					,						__UpperCamelCase=0.0					,						__UpperCamelCase=True					,						__UpperCamelCase="relu"					,						__UpperCamelCase=2_5_6					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1.0					,						__UpperCamelCase=True					,						__UpperCamelCase=False					,						__UpperCamelCase="sine"					,						__UpperCamelCase=5					,						__UpperCamelCase=4					,						__UpperCamelCase=4					,						__UpperCamelCase=True					,						__UpperCamelCase=3_0_0					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=1					,						__UpperCamelCase=5					,						__UpperCamelCase=2					,						__UpperCamelCase=1					,						__UpperCamelCase=1					,						__UpperCamelCase=5					,						__UpperCamelCase=2					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.2_5					,						**__UpperCamelCase					,						)->	Dict:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
                __lowerCAmelCase								=					CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]	)
           else:
                if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					backbone_config.pop("model_type"	)
                     __lowerCAmelCase								=					CONFIG_MAPPING[backbone_model_type]
                     __lowerCAmelCase								=					config_class.from_dict(__UpperCamelCase	)

           __lowerCAmelCase								=					backbone_config
           __lowerCAmelCase								=					num_queries
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					d_model
           __lowerCAmelCase								=					encoder_ffn_dim
           __lowerCAmelCase								=					encoder_layers
           __lowerCAmelCase								=					encoder_attention_heads
           __lowerCAmelCase								=					decoder_ffn_dim
           __lowerCAmelCase								=					decoder_layers
           __lowerCAmelCase								=					decoder_attention_heads
           __lowerCAmelCase								=					dropout
           __lowerCAmelCase								=					attention_dropout
           __lowerCAmelCase								=					activation_dropout
           __lowerCAmelCase								=					activation_function
           __lowerCAmelCase								=					init_std
           __lowerCAmelCase								=					init_xavier_std
           __lowerCAmelCase								=					encoder_layerdrop
           __lowerCAmelCase								=					auxiliary_loss
           __lowerCAmelCase								=					position_embedding_type
           # deformable attributes
           __lowerCAmelCase								=					num_feature_levels
           __lowerCAmelCase								=					encoder_n_points
           __lowerCAmelCase								=					decoder_n_points
           __lowerCAmelCase								=					two_stage
           __lowerCAmelCase								=					two_stage_num_proposals
           __lowerCAmelCase								=					with_box_refine
           __lowerCAmelCase								=					assign_first_stage
           if two_stage is True and with_box_refine is False:
                raise ValueError("If two_stage is True, with_box_refine must be True."	)
           # Hungarian matcher
           __lowerCAmelCase								=					class_cost
           __lowerCAmelCase								=					bbox_cost
           __lowerCAmelCase								=					giou_cost
           # Loss coefficients
           __lowerCAmelCase								=					mask_loss_coefficient
           __lowerCAmelCase								=					dice_loss_coefficient
           __lowerCAmelCase								=					bbox_loss_coefficient
           __lowerCAmelCase								=					giou_loss_coefficient
           __lowerCAmelCase								=					eos_coefficient
           __lowerCAmelCase								=					focal_alpha
           super().__init__(is_encoder_decoder=__UpperCamelCase					,						**__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return self.encoder_attention_heads


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return self.d_model


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					copy.deepcopy(self.__dict__	)
           __lowerCAmelCase								=					self.backbone_config.to_dict()
           __lowerCAmelCase								=					self.__class__.model_type
           return output














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


if __name__ == "__main__":
 lowerCamelCase			:					List[str]         =	input('''Enter numbers separated by a comma:\n''').strip()
 lowerCamelCase			:					Tuple         =	[int(item) for item in user_input.split(''',''')]
 print(pancake_sort(unsorted))






