


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}





class  _a							(    _lowerCamelCase):
	_a				   =							'''conditional_detr'''
	_a				   =							['''past_key_values''']
	_a				   =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=True		, _SCREAMING_SNAKE_CASE						:		List[Any]=None		, _SCREAMING_SNAKE_CASE						:		Tuple=3		, _SCREAMING_SNAKE_CASE						:		Dict=300		, _SCREAMING_SNAKE_CASE						:		Optional[int]=6		, _SCREAMING_SNAKE_CASE						:		List[Any]=2048		, _SCREAMING_SNAKE_CASE						:		List[str]=8		, _SCREAMING_SNAKE_CASE						:		Tuple=6		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=2048		, _SCREAMING_SNAKE_CASE						:		Optional[int]=8		, _SCREAMING_SNAKE_CASE						:		str=0.0		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.0		, _SCREAMING_SNAKE_CASE						:		str=True		, _SCREAMING_SNAKE_CASE						:		Tuple="relu"		, _SCREAMING_SNAKE_CASE						:		Any=256		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		int=0.0		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.0		, _SCREAMING_SNAKE_CASE						:		List[str]=0.02		, _SCREAMING_SNAKE_CASE						:		Dict=1.0		, _SCREAMING_SNAKE_CASE						:		Tuple=False		, _SCREAMING_SNAKE_CASE						:		Dict="sine"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="resnet50"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=True		, _SCREAMING_SNAKE_CASE						:		Tuple=False		, _SCREAMING_SNAKE_CASE						:		List[str]=2		, _SCREAMING_SNAKE_CASE						:		List[Any]=5		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		List[Any]=1		, _SCREAMING_SNAKE_CASE						:		Dict=1		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Dict=5		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		List[str]=0.25		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	Optional[int]:


			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__       :      List[str]   					= CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
					elif isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
							lowerCAmelCase__       :      List[Any]   					= backbone_config.get('''model_type'''		)
							lowerCAmelCase__       :      Optional[int]   					= CONFIG_MAPPING[backbone_model_type]
							lowerCAmelCase__       :      Optional[Any]   					= config_class.from_dict(_SCREAMING_SNAKE_CASE		)

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




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Dict:
			return self.encoder_attention_heads




	@property
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Tuple:
			return self.d_model




	def 							UpperCAmelCase__( self						:		Any		)->	List[str]:
			lowerCAmelCase__       :      Tuple   					= copy.deepcopy(self.__dict__		)
			if self.backbone_config is not None:
					lowerCAmelCase__       :      Dict   					= self.backbone_config.to_dict()
			lowerCAmelCase__       :      Tuple   					= self.__class__.model_type
			return output





class  _a							(    _lowerCamelCase):
	_a				   =							version.parse('''1.11''')




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




	@property
	def 							UpperCAmelCase__( self						:		str		)->	Any:
			return 1E-5




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	Dict:
			return 12








def 					lowerCamelCase_   (	_a = 4_000_000	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= 0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(_a	)
				lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= b, a + b
		return sum(_a	)


if __name__ == "__main__":
		print(f'''{solution() = }''')




