



"""simple docstring"""






import copy
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


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

__snake_case				:  Dict =       {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'conditional_detr'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']
	SCREAMING_SNAKE_CASE        =      {
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}

	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int]=True			,     _SCREAMING_SNAKE_CASE:						int=None			,     _SCREAMING_SNAKE_CASE:						int=3			,     _SCREAMING_SNAKE_CASE:						List[Any]=300			,     _SCREAMING_SNAKE_CASE:						Optional[int]=6			,     _SCREAMING_SNAKE_CASE:						Any=2048			,     _SCREAMING_SNAKE_CASE:						Optional[int]=8			,     _SCREAMING_SNAKE_CASE:						Any=6			,     _SCREAMING_SNAKE_CASE:						List[str]=2048			,     _SCREAMING_SNAKE_CASE:						List[Any]=8			,     _SCREAMING_SNAKE_CASE:						int=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.0			,     _SCREAMING_SNAKE_CASE:						str=True			,     _SCREAMING_SNAKE_CASE:						List[str]="relu"			,     _SCREAMING_SNAKE_CASE:						Any=256			,     _SCREAMING_SNAKE_CASE:						str=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.02			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=1.0			,     _SCREAMING_SNAKE_CASE:						Optional[int]=False			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="sine"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="resnet50"			,     _SCREAMING_SNAKE_CASE:						str=True			,     _SCREAMING_SNAKE_CASE:						List[Any]=False			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     _SCREAMING_SNAKE_CASE:						str=5			,     _SCREAMING_SNAKE_CASE:						str=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						List[str]=1			,     _SCREAMING_SNAKE_CASE:						Optional[int]=2			,     _SCREAMING_SNAKE_CASE:						Optional[int]=5			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.25			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			Any:

				"""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.")
										__lowerCAmelCase :    List[Any]				=			CONFIG_MAPPING["resnet"](out_features=["stage4"])
							elif isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
										__lowerCAmelCase :    List[Any]				=			backbone_config.get("model_type")
										__lowerCAmelCase :    Any				=			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase :    Dict				=			config_class.from_dict(_SCREAMING_SNAKE_CASE)

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

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

				"""simple docstring"""

				return self.encoder_attention_heads

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			int:

				"""simple docstring"""

				return self.d_model







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Optional[Any]:

				"""simple docstring"""

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






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      version.parse('1.11'				)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

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

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			float:

				"""simple docstring"""

				return 1e-5







	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			int:

				"""simple docstring"""

				return 12




"""simple docstring"""






# Function to print upper half of diamond (pyramid)


def      _lowercase							(    __snake_case      )		->  Dict:
			for i in range(0   ,__snake_case      ):
						for _ in range(0   ,n - i - 1      ):  # printing spaces
									print(" "   ,end=""      )
						for _ in range(0   ,i + 1      ):  # printing stars
									print("* "   ,end=""      )
						print()


def      _lowercase							(    __snake_case      )		->  Tuple:
			for i in range(__snake_case   ,0   ,-1      ):
						for _ in range(__snake_case   ,0   ,-1      ):  # printing stars
									print("* "   ,end=""      )
						print()
						for _ in range(n - i + 1   ,0   ,-1      ):  # printing spaces
									print(" "   ,end=""      )


def      _lowercase							(    __snake_case      )		->  Any:
			if n <= 0:
						print("       ...       ....        nothing printing :("      )
						return
			floyd(__snake_case      )  # upper half
			reverse_floyd(__snake_case      )  # lower half


if __name__ == "__main__":
	print(R'| /\ | |- |  |-  |--| |\  /| |-')
	print(R'|/  \| |- |_ |_  |__| | \/ | |_')
	__snake_case				:  Tuple =       1
	while K:
		__snake_case				:  List[str] =       int(input('enter the number and , and see the magic : '))
		print()
		pretty_print(user_number)
		__snake_case				:  Optional[Any] =       int(input('press 0 to exit... and 1 to continue...'))

	print('Good Bye...')