

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     len(SCREAMING_SNAKE_CASE_  )
      for i in range(length - 1  ):
            SCREAMING_SNAKE_CASE_				:  Any           =     i
            for k in range(i + 1					,   SCREAMING_SNAKE_CASE_  ):
                  if collection[k] < collection[least]:
                        SCREAMING_SNAKE_CASE_				:  Any           =     k
            if least != i:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     (collection[i], collection[least])
      return collection


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







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase     :   Dict						     =  logging.get_logger(__name__)

lowerCAmelCase     :   str						     =  {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  '''table-transformer'''
  SCREAMING_SNAKE_CASE    :      Any       =  ['''past_key_values''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  {
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=100 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=256 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1.0 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="sine" ,					_SCREAMING_SNAKE_CASE="resnet50" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        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_				:  List[Any]           =     CONFIG_MAPPING['resnet'](out_features=['stage4']					)
              elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone_config.get('model_type'					)
                    SCREAMING_SNAKE_CASE_				:  str           =     CONFIG_MAPPING[backbone_model_type]
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     config_class.from_dict(_SCREAMING_SNAKE_CASE					)
              # set timm attributes to None
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None, None, None

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

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder_attention_heads



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.d_model

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  version.parse('''1.11''')

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-5



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 12






