

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowercase_     :   Any 				=				logging.get_logger(__name__)

lowercase_     :   Dict 				=				{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class 		_lowerCamelCase		(   __lowerCamelCase    ):
    __a  =      "detr"
    __a  =      ["past_key_values"]
    __a  =      {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }
    def __init__(  self , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=3 , lowerCAmelCase=100 , lowerCAmelCase=6 , lowerCAmelCase=2048 , lowerCAmelCase=8 , lowerCAmelCase=6 , lowerCAmelCase=2048 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=True , lowerCAmelCase="relu" , lowerCAmelCase=256 , lowerCAmelCase=0.1 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=0.02 , lowerCAmelCase=1.0 , lowerCAmelCase=False , lowerCAmelCase="sine" , lowerCAmelCase="resnet50" , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=1 , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=0.1 , **lowerCAmelCase , )       ->					Optional[int]:
     if backbone_config is not None and use_timm_backbone:
      raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

     if not use_timm_backbone:
      if backbone_config is None:
       logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
       SCREAMING_SNAKE_CASE__:   Optional[int]=      CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
      elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_   ):
       SCREAMING_SNAKE_CASE__:   Any=      backbone_config.get('''model_type'''   )
       SCREAMING_SNAKE_CASE__:   Dict=      CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      config_class.from_dict(SCREAMING_SNAKE_CASE_   )
      # set timm attributes to None
      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None, None, None

     SCREAMING_SNAKE_CASE__:   str=      use_timm_backbone
     SCREAMING_SNAKE_CASE__:   Tuple=      backbone_config
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      num_channels
     SCREAMING_SNAKE_CASE__:   List[str]=      num_queries
     SCREAMING_SNAKE_CASE__:   List[str]=      d_model
     SCREAMING_SNAKE_CASE__:   Any=      encoder_ffn_dim
     SCREAMING_SNAKE_CASE__:   List[str]=      encoder_layers
     SCREAMING_SNAKE_CASE__:   Any=      encoder_attention_heads
     SCREAMING_SNAKE_CASE__:   Dict=      decoder_ffn_dim
     SCREAMING_SNAKE_CASE__:   Dict=      decoder_layers
     SCREAMING_SNAKE_CASE__:   str=      decoder_attention_heads
     SCREAMING_SNAKE_CASE__:   Optional[int]=      dropout
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      attention_dropout
     SCREAMING_SNAKE_CASE__:   int=      activation_dropout
     SCREAMING_SNAKE_CASE__:   Any=      activation_function
     SCREAMING_SNAKE_CASE__:   Tuple=      init_std
     SCREAMING_SNAKE_CASE__:   List[Any]=      init_xavier_std
     SCREAMING_SNAKE_CASE__:   Optional[int]=      encoder_layerdrop
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      decoder_layerdrop
     SCREAMING_SNAKE_CASE__:   Optional[int]=      encoder_layers
     SCREAMING_SNAKE_CASE__:   str=      auxiliary_loss
     SCREAMING_SNAKE_CASE__:   List[str]=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   int=      backbone
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      use_pretrained_backbone
     SCREAMING_SNAKE_CASE__:   Optional[int]=      dilation
     # Hungarian matcher
     SCREAMING_SNAKE_CASE__:   int=      class_cost
     SCREAMING_SNAKE_CASE__:   List[Any]=      bbox_cost
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      giou_cost
     # Loss coefficients
     SCREAMING_SNAKE_CASE__:   List[str]=      mask_loss_coefficient
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      dice_loss_coefficient
     SCREAMING_SNAKE_CASE__:   Optional[int]=      bbox_loss_coefficient
     SCREAMING_SNAKE_CASE__:   Tuple=      giou_loss_coefficient
     SCREAMING_SNAKE_CASE__:   Dict=      eos_coefficient
     super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return self.encoder_attention_heads
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return self.d_model
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase , **lowerCAmelCase   )       ->					Optional[int]:
     return cls(backbone_config=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_   )



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      copy.deepcopy(self.__dict__   )
     if output["backbone_config"] is not None:
      SCREAMING_SNAKE_CASE__:   List[str]=      self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE__:   int=      self.__class__.model_type
     return output






class 		_lowerCamelCase		(   __lowerCamelCase    ):
    __a  =      version.parse("1.11"    )
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ]   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Any:
     return 1e-5



    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     return 12


def 			A__      (	snake_case_ :		list      ):

 if len(snake_case_      ) < 2:
  return collection

 def circle_sort_util(snake_case_ :		list       ,				snake_case_ :		int       ,				snake_case_ :		int      ) -> bool:
  SCREAMING_SNAKE_CASE__:   Dict=      False

  if low == high:
   return swapped

  SCREAMING_SNAKE_CASE__:   str=      low
  SCREAMING_SNAKE_CASE__:   Optional[int]=      high

  while left < right:
   if collection[left] > collection[right]:
    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      (
        collection[right],
        collection[left],
    )
    SCREAMING_SNAKE_CASE__:   List[Any]=      True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      (
       collection[right + 1],
       collection[left],
   )

   SCREAMING_SNAKE_CASE__:   List[str]=      True

  SCREAMING_SNAKE_CASE__:   Dict=      low + int((high - low) / 2      )
  SCREAMING_SNAKE_CASE__:   str=      circle_sort_util(snake_case_       ,				snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[Any]=      circle_sort_util(snake_case_       ,				mid + 1       ,				snake_case_      )

  return swapped or left_swap or right_swap

 SCREAMING_SNAKE_CASE__:   Optional[int]=      True

 while is_not_sorted is True:
  SCREAMING_SNAKE_CASE__:   Tuple=      circle_sort_util(snake_case_       ,				0       ,				len(snake_case_      ) - 1      )

 return collection


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

