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


__UpperCamelCase	: int    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    '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       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""detr"""
						UpperCamelCase_  =	["""past_key_values"""]
						UpperCamelCase_  =	{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       List[Any]=None					,	UpperCamelCase__			:       List[str]=3					,	UpperCamelCase__			:       Any=100					,	UpperCamelCase__			:       Union[str, Any]=6					,	UpperCamelCase__			:       Union[str, Any]=2048					,	UpperCamelCase__			:       str=8					,	UpperCamelCase__			:       Dict=6					,	UpperCamelCase__			:       Tuple=2048					,	UpperCamelCase__			:       Optional[int]=8					,	UpperCamelCase__			:       int=0.0					,	UpperCamelCase__			:       Union[str, Any]=0.0					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       Union[str, Any]="relu"					,	UpperCamelCase__			:       str=256					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       List[str]=0.0					,	UpperCamelCase__			:       Union[str, Any]=0.0					,	UpperCamelCase__			:       int=0.02					,	UpperCamelCase__			:       Union[str, Any]=1.0					,	UpperCamelCase__			:       str=False					,	UpperCamelCase__			:       int="sine"					,	UpperCamelCase__			:       List[str]="resnet50"					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Tuple=False					,	UpperCamelCase__			:       Dict=1					,	UpperCamelCase__			:       Optional[Any]=5					,	UpperCamelCase__			:       Optional[int]=2					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=1					,	UpperCamelCase__			:       str=5					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       Union[str, Any]=0.1					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''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							:					int           =							CONFIG_MAPPING["resnet"](out_features=['''stage4''']		)
																		elif isinstance(__A					,	__A		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							backbone_config.get('''model_type'''		)
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							CONFIG_MAPPING[backbone_model_type]
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							config_class.from_dict(__A		)
																		# set timm attributes to None
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							None, None, None

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


						@property
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												return self.encoder_attention_heads


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												return self.d_model


						@classmethod
						def  __A ( cls			:       str					,	UpperCamelCase__			:       PretrainedConfig					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return cls(backbone_config=__A					,	**__A		)



						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.11""")


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												        ('''pixel_mask''', {0: '''batch'''}),
												    ]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 1E-5



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





												'''simple docstring'''





												return 12




import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





