



"""simple docstring"""

import copy

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


A_						:    List[Any]				=      logging.get_logger(__name__)

A_						:    List[str]				=      {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= '''deformable_detr'''
	_SCREAMING_SNAKE_CASE	:		Dict  						= {
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Tuple=True     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[str]=3     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=300     ,       _SCREAMING_SNAKE_CASE				:       Tuple=1_024     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=6     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1_024     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=8     ,       _SCREAMING_SNAKE_CASE				:       str=6     ,       _SCREAMING_SNAKE_CASE				:       Tuple=1_024     ,       _SCREAMING_SNAKE_CASE				:       int=8     ,       _SCREAMING_SNAKE_CASE				:       int=0.0     ,       _SCREAMING_SNAKE_CASE				:       Any=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple="relu"     ,       _SCREAMING_SNAKE_CASE				:       Any=256     ,       _SCREAMING_SNAKE_CASE				:       int=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0.0     ,       _SCREAMING_SNAKE_CASE				:       Tuple=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       int=1.0     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=False     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="sine"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="resnet50"     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=False     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=4     ,       _SCREAMING_SNAKE_CASE				:       Tuple=4     ,       _SCREAMING_SNAKE_CASE				:       List[str]=4     ,       _SCREAMING_SNAKE_CASE				:       Tuple=False     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=300     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=5     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       int=1     ,       _SCREAMING_SNAKE_CASE				:       int=1     ,       _SCREAMING_SNAKE_CASE				:       str=5     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Tuple=0.2_5     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=False     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       )						->	int:





					"""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						:							str							=     CONFIG_MAPPING['resnet'](out_features=['stage4']    )
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													SCREAMING_SNAKE_CASE						:							int							=     backbone_config.get('model_type'    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     config_class.from_dict(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     use_timm_backbone
					SCREAMING_SNAKE_CASE						:							List[Any]							=     backbone_config
					SCREAMING_SNAKE_CASE						:							Tuple							=     num_channels
					SCREAMING_SNAKE_CASE						:							str							=     num_queries
					SCREAMING_SNAKE_CASE						:							List[Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     d_model
					SCREAMING_SNAKE_CASE						:							Any							=     encoder_ffn_dim
					SCREAMING_SNAKE_CASE						:							str							=     encoder_layers
					SCREAMING_SNAKE_CASE						:							List[str]							=     encoder_attention_heads
					SCREAMING_SNAKE_CASE						:							Tuple							=     decoder_ffn_dim
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     decoder_layers
					SCREAMING_SNAKE_CASE						:							int							=     decoder_attention_heads
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     dropout
					SCREAMING_SNAKE_CASE						:							str							=     attention_dropout
					SCREAMING_SNAKE_CASE						:							List[str]							=     activation_dropout
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     activation_function
					SCREAMING_SNAKE_CASE						:							Any							=     init_std
					SCREAMING_SNAKE_CASE						:							str							=     init_xavier_std
					SCREAMING_SNAKE_CASE						:							Tuple							=     encoder_layerdrop
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     auxiliary_loss
					SCREAMING_SNAKE_CASE						:							List[str]							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							Dict							=     backbone
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     use_pretrained_backbone
					SCREAMING_SNAKE_CASE						:							Dict							=     dilation
					# deformable attributes
					SCREAMING_SNAKE_CASE						:							str							=     num_feature_levels
					SCREAMING_SNAKE_CASE						:							Any							=     encoder_n_points
					SCREAMING_SNAKE_CASE						:							int							=     decoder_n_points
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     two_stage
					SCREAMING_SNAKE_CASE						:							int							=     two_stage_num_proposals
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     with_box_refine
					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
					SCREAMING_SNAKE_CASE						:							List[str]							=     class_cost
					SCREAMING_SNAKE_CASE						:							List[Any]							=     bbox_cost
					SCREAMING_SNAKE_CASE						:							str							=     giou_cost
					# Loss coefficients
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     mask_loss_coefficient
					SCREAMING_SNAKE_CASE						:							Dict							=     dice_loss_coefficient
					SCREAMING_SNAKE_CASE						:							Dict							=     bbox_loss_coefficient
					SCREAMING_SNAKE_CASE						:							str							=     giou_loss_coefficient
					SCREAMING_SNAKE_CASE						:							int							=     eos_coefficient
					SCREAMING_SNAKE_CASE						:							List[str]							=     focal_alpha
					SCREAMING_SNAKE_CASE						:							int							=     disable_custom_kernels
					super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	@property
	def 			_lowerCAmelCase     (      self				:       int    )						->	int:





					"""simple docstring"""


					return self.encoder_attention_heads





	@property
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	int:





					"""simple docstring"""


					return self.d_model







	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     copy.deepcopy(self.__dict__    )
					if self.backbone_config is not None:
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.backbone_config.to_dict()
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.__class__.model_type
					return output










"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





