import copy

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


__UpperCamelCase	: List[str]    		=  logging.get_logger(__name__)

__UpperCamelCase	: 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       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deformable_detr"""
						UpperCamelCase_  =	{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]=True					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Union[str, Any]=3					,	UpperCamelCase__			:       List[str]=300					,	UpperCamelCase__			:       Any=1024					,	UpperCamelCase__			:       Optional[Any]=6					,	UpperCamelCase__			:       Any=1024					,	UpperCamelCase__			:       int=8					,	UpperCamelCase__			:       Dict=6					,	UpperCamelCase__			:       int=1024					,	UpperCamelCase__			:       Optional[int]=8					,	UpperCamelCase__			:       int=0.0					,	UpperCamelCase__			:       Any=True					,	UpperCamelCase__			:       str="relu"					,	UpperCamelCase__			:       Optional[Any]=256					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       str=0.0					,	UpperCamelCase__			:       str=0.0					,	UpperCamelCase__			:       Optional[int]=0.02					,	UpperCamelCase__			:       Dict=1.0					,	UpperCamelCase__			:       int=True					,	UpperCamelCase__			:       int=False					,	UpperCamelCase__			:       str="sine"					,	UpperCamelCase__			:       Any="resnet50"					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       List[Any]=False					,	UpperCamelCase__			:       Tuple=4					,	UpperCamelCase__			:       Any=4					,	UpperCamelCase__			:       str=4					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       List[str]=300					,	UpperCamelCase__			:       Union[str, Any]=False					,	UpperCamelCase__			:       str=1					,	UpperCamelCase__			:       Optional[int]=5					,	UpperCamelCase__			:       Union[str, Any]=2					,	UpperCamelCase__			:       Optional[int]=1					,	UpperCamelCase__			:       str=1					,	UpperCamelCase__			:       Dict=5					,	UpperCamelCase__			:       int=2					,	UpperCamelCase__			:       str=0.1					,	UpperCamelCase__			:       int=0.25					,	UpperCamelCase__			:       Optional[int]=False					,	**UpperCamelCase__			:       Dict					,	):





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


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												return self.encoder_attention_heads


						@property
						def  __A ( self			:       Any		):





												'''simple docstring'''





												return self.d_model



						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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






from __future__ import annotations

from typing import Any





class       lowercase__		(     UpperCamelCase_):
						pass






class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							data
												SCREAMING_SNAKE_CASE							:					Node | None           =							None


						def __iter__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self
												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(UpperCamelCase__		)
																		yield node.data
																		SCREAMING_SNAKE_CASE							:					Dict           =							node.next_node



						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												try:
																		list(self		)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  Node(1)
					__UpperCamelCase	: str    		=  Node(2)
					__UpperCamelCase	: Dict    		=  Node(3)
					__UpperCamelCase	: List[Any]    		=  Node(4)
					print(root_node.has_loop)  # False
					__UpperCamelCase	: int    		=  root_node.next_node
					print(root_node.has_loop)  # True

					__UpperCamelCase	: Union[str, Any]    		=  Node(5)
					__UpperCamelCase	: Union[str, Any]    		=  Node(6)
					__UpperCamelCase	: List[Any]    		=  Node(5)
					__UpperCamelCase	: List[str]    		=  Node(6)
					print(root_node.has_loop)  # False

					__UpperCamelCase	: List[Any]    		=  Node(1)
					print(root_node.has_loop)  # False





