



"""simple docstring"""
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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"detr"
							__lowerCAmelCase    					=					["past_key_values"]
							__lowerCAmelCase    					=					{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}






							def __init__(   self			,     a_=True			,     a_=None			,     a_=3			,     a_=1_0_0			,     a_=6			,     a_=2_0_4_8			,     a_=8			,     a_=6			,     a_=2_0_4_8			,     a_=8			,     a_=0.0			,     a_=0.0			,     a_=True			,     a_="relu"			,     a_=2_5_6			,     a_=0.1			,     a_=0.0			,     a_=0.0			,     a_=0.02			,     a_=1.0			,     a_=False			,     a_="sine"			,     a_="resnet50"			,     a_=True			,     a_=False			,     a_=1			,     a_=5			,     a_=2			,     a_=1			,     a_=1			,     a_=5			,     a_=2			,     a_=0.1			,     **a_			,     ):
								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."      )
										a_     :      Union[str, Any] 						=     CONFIG_MAPPING["resnet"](out_features=["stage4"]      )
									elif isinstance(a_			,     a_      ):
										a_     :      Any 						=     backbone_config.get("model_type"      )
										a_     :      Union[str, Any] 						=     CONFIG_MAPPING[backbone_model_type]
										a_     :      List[str] 						=     config_class.from_dict(a_      )
									# set timm attributes to None
									a_     :      Optional[Any] 						=     None, None, None

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






							@property
							def snake_case_  (   self      ):
								return self.encoder_attention_heads






							@property
							def snake_case_  (   self      ):
								return self.d_model






							@classmethod
							def snake_case_  (   cls			,     a_			,     **a_      ):
								return cls(backbone_config=a_			,     **a_      )






							def snake_case_  (   self      ):
								a_     :      List[str] 						=     copy.deepcopy(self.__dict__      )
								if output["backbone_config"] is not None:
									a_     :      Union[str, Any] 						=     self.backbone_config.to_dict()
								a_     :      Union[str, Any] 						=     self.__class__.model_type
								return output




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					version.parse("1.11"				)






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






							@property
							def snake_case_  (   self      ):
								return 1e-5






							@property
							def snake_case_  (   self      ):
								return 1_2




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()