



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_	       =  logging.get_logger(__name__)

lowercase_	       =  {
    """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 						__a (     __UpperCAmelCase				):
	SCREAMING_SNAKE_CASE       =       "detr"
	SCREAMING_SNAKE_CASE       =       ["past_key_values"]
	SCREAMING_SNAKE_CASE       =       {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}



	def __init__(						self							:    Dict				,	snake_case_							:    List[str]=True				,	snake_case_							:    Dict=None				,	snake_case_							:    int=3				,	snake_case_							:    int=1_00				,	snake_case_							:    Tuple=6				,	snake_case_							:    List[Any]=20_48				,	snake_case_							:    Union[str, Any]=8				,	snake_case_							:    Optional[Any]=6				,	snake_case_							:    Tuple=20_48				,	snake_case_							:    Union[str, Any]=8				,	snake_case_							:    List[Any]=0.0				,	snake_case_							:    str=0.0				,	snake_case_							:    Optional[int]=True				,	snake_case_							:    List[Any]="relu"				,	snake_case_							:    Optional[int]=2_56				,	snake_case_							:    Optional[int]=0.1				,	snake_case_							:    Tuple=0.0				,	snake_case_							:    Union[str, Any]=0.0				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    int=1.0				,	snake_case_							:    List[Any]=False				,	snake_case_							:    Optional[int]="sine"				,	snake_case_							:    List[Any]="resnet50"				,	snake_case_							:    Optional[int]=True				,	snake_case_							:    int=False				,	snake_case_							:    List[str]=1				,	snake_case_							:    Tuple=5				,	snake_case_							:    Tuple=2				,	snake_case_							:    List[str]=1				,	snake_case_							:    str=1				,	snake_case_							:    Dict=5				,	snake_case_							:    int=2				,	snake_case_							:    int=0.1				,	**snake_case_							:    str				,	)->							List[str]:

						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	   =CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
											elif isinstance(snake_case_				,	snake_case_):
																__lowerCAmelCase	   =backbone_config.get("""model_type""")
																__lowerCAmelCase	   =CONFIG_MAPPING[backbone_model_type]
																__lowerCAmelCase	   =config_class.from_dict(snake_case_)
											# set timm attributes to None
											__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =None, None, None

						__lowerCAmelCase	   =use_timm_backbone
						__lowerCAmelCase	   =backbone_config
						__lowerCAmelCase	   =num_channels
						__lowerCAmelCase	   =num_queries
						__lowerCAmelCase	   =d_model
						__lowerCAmelCase	   =encoder_ffn_dim
						__lowerCAmelCase	   =encoder_layers
						__lowerCAmelCase	   =encoder_attention_heads
						__lowerCAmelCase	   =decoder_ffn_dim
						__lowerCAmelCase	   =decoder_layers
						__lowerCAmelCase	   =decoder_attention_heads
						__lowerCAmelCase	   =dropout
						__lowerCAmelCase	   =attention_dropout
						__lowerCAmelCase	   =activation_dropout
						__lowerCAmelCase	   =activation_function
						__lowerCAmelCase	   =init_std
						__lowerCAmelCase	   =init_xavier_std
						__lowerCAmelCase	   =encoder_layerdrop
						__lowerCAmelCase	   =decoder_layerdrop
						__lowerCAmelCase	   =encoder_layers
						__lowerCAmelCase	   =auxiliary_loss
						__lowerCAmelCase	   =position_embedding_type
						__lowerCAmelCase	   =backbone
						__lowerCAmelCase	   =use_pretrained_backbone
						__lowerCAmelCase	   =dilation
						# Hungarian matcher
						__lowerCAmelCase	   =class_cost
						__lowerCAmelCase	   =bbox_cost
						__lowerCAmelCase	   =giou_cost
						# Loss coefficients
						__lowerCAmelCase	   =mask_loss_coefficient
						__lowerCAmelCase	   =dice_loss_coefficient
						__lowerCAmelCase	   =bbox_loss_coefficient
						__lowerCAmelCase	   =giou_loss_coefficient
						__lowerCAmelCase	   =eos_coefficient
						super().__init__(is_encoder_decoder=snake_case_				,	**snake_case_)



	@property
	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						return self.encoder_attention_heads



	@property
	def 		UpperCamelCase							(						self							:    List[Any])->							List[str]:
						return self.d_model



	@classmethod
	def 		UpperCamelCase							(						cls							:    List[Any]				,	snake_case_							:    int				,	**snake_case_							:    int)->							str:
						return cls(backbone_config=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =copy.deepcopy(self.__dict__)
						if output["backbone_config"] is not None:
											__lowerCAmelCase	   =self.backbone_config.to_dict()
						__lowerCAmelCase	   =self.__class__.model_type
						return output






class 						__a (     __UpperCAmelCase				):
	SCREAMING_SNAKE_CASE       =       version.parse("1.11"				)



	@property
	def 		UpperCamelCase							(						self							:    Optional[int])->							str:
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""pixel_mask""", {0: """batch"""}),
						    ])



	@property
	def 		UpperCamelCase							(						self							:    List[str])->							Any:
						return 1e-5



	@property
	def 		UpperCamelCase							(						self							:    str)->							Dict:
						return 12









import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase_	       =			TypeVar('''T''')

lowercase_	       =			Union[List[T], Tuple[T, ...]]
lowercase_	       =			Union[T, List[T], Dict[str, T]]
lowercase_	       =			Union[str, bytes, os.PathLike]




