
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip

import copy

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


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      int          =			{
    '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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"deformable_detr"
	_lowerCAmelCase							       =				{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}





	def __init__(			self				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=3				,       UpperCamelCase__=300				,       UpperCamelCase__=1024				,       UpperCamelCase__=6				,       UpperCamelCase__=1024				,       UpperCamelCase__=8				,       UpperCamelCase__=6				,       UpperCamelCase__=1024				,       UpperCamelCase__=8				,       UpperCamelCase__=0.0				,       UpperCamelCase__=True				,       UpperCamelCase__="relu"				,       UpperCamelCase__=256				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1.0				,       UpperCamelCase__=True				,       UpperCamelCase__=False				,       UpperCamelCase__="sine"				,       UpperCamelCase__="resnet50"				,       UpperCamelCase__=True				,       UpperCamelCase__=False				,       UpperCamelCase__=4				,       UpperCamelCase__=4				,       UpperCamelCase__=4				,       UpperCamelCase__=False				,       UpperCamelCase__=300				,       UpperCamelCase__=False				,       UpperCamelCase__=1				,       UpperCamelCase__=5				,       UpperCamelCase__=2				,       UpperCamelCase__=1				,       UpperCamelCase__=1				,       UpperCamelCase__=5				,       UpperCamelCase__=2				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.2_5				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					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__  :	int         =							CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Union[str, Any]         =							backbone_config.get('''model_type'''		)
													A__  :	Union[str, Any]         =							CONFIG_MAPPING[backbone_model_type]
													A__  :	Optional[int]         =							config_class.from_dict(UpperCamelCase__		)
					A__  :	Tuple         =							use_timm_backbone
					A__  :	int         =							backbone_config
					A__  :	List[Any]         =							num_channels
					A__  :	List[Any]         =							num_queries
					A__  :	str         =							max_position_embeddings
					A__  :	Tuple         =							d_model
					A__  :	int         =							encoder_ffn_dim
					A__  :	Union[str, Any]         =							encoder_layers
					A__  :	Optional[Any]         =							encoder_attention_heads
					A__  :	List[Any]         =							decoder_ffn_dim
					A__  :	Tuple         =							decoder_layers
					A__  :	Optional[Any]         =							decoder_attention_heads
					A__  :	List[str]         =							dropout
					A__  :	str         =							attention_dropout
					A__  :	List[Any]         =							activation_dropout
					A__  :	Any         =							activation_function
					A__  :	Optional[Any]         =							init_std
					A__  :	Union[str, Any]         =							init_xavier_std
					A__  :	Union[str, Any]         =							encoder_layerdrop
					A__  :	Optional[int]         =							auxiliary_loss
					A__  :	str         =							position_embedding_type
					A__  :	List[Any]         =							backbone
					A__  :	Optional[Any]         =							use_pretrained_backbone
					A__  :	Any         =							dilation
					# deformable attributes
					A__  :	List[Any]         =							num_feature_levels
					A__  :	List[str]         =							encoder_n_points
					A__  :	int         =							decoder_n_points
					A__  :	List[Any]         =							two_stage
					A__  :	Dict         =							two_stage_num_proposals
					A__  :	Optional[int]         =							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
					A__  :	List[str]         =							class_cost
					A__  :	List[Any]         =							bbox_cost
					A__  :	Any         =							giou_cost
					# Loss coefficients
					A__  :	List[str]         =							mask_loss_coefficient
					A__  :	Union[str, Any]         =							dice_loss_coefficient
					A__  :	List[Any]         =							bbox_loss_coefficient
					A__  :	Tuple         =							giou_loss_coefficient
					A__  :	Optional[Any]         =							eos_coefficient
					A__  :	List[Any]         =							focal_alpha
					A__  :	List[str]         =							disable_custom_kernels
					super().__init__(is_encoder_decoder=UpperCamelCase__				,       **UpperCamelCase__		)





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





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





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