






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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	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_00			,     a=6			,     a=20_48			,     a=8			,     a=6			,     a=20_48			,     a=8			,     a=0.0			,     a=0.0			,     a=True			,     a="relu"			,     a=2_56			,     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			,     )			->       Union[str, Any]:
								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.'   )
																						snake_case_           =				CONFIG_MAPPING['resnet'](out_features=['stage4']   )
															elif isinstance(a			,     a   ):
																						snake_case_           =				backbone_config.get('model_type'   )
																						snake_case_           =				CONFIG_MAPPING[backbone_model_type]
																						snake_case_           =				config_class.from_dict(a   )
															# set timm attributes to None
															snake_case_     ,						snake_case_     ,						snake_case_           =				None, None, None

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




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								return self.encoder_attention_heads




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								return self.d_model




	@classmethod
	def 				_UpperCamelCase     (			cls			,     a			,     **a   )			->       Union[str, Any]:
								return cls(backbone_config=a			,     **a   )






	def 				_UpperCamelCase     (			self   )			->       Dict[str, any]:
								snake_case_           =				copy.deepcopy(self.__dict__   )
								if output["backbone_config"] is not None:
															snake_case_           =				self.backbone_config.to_dict()
								snake_case_           =				self.__class__.model_type
								return output






class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					version.parse('''1.11'''							)




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




	@property
	def 				_UpperCamelCase     (			self   )			->       float:
								return 1E-5






	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								return 12













class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





