





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


lowercase_		:				Dict				=						logging.get_logger(__name__)

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


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	"conditional_detr"
						_UpperCamelCase  :	List[str]								=	["past_key_values"]
						_UpperCamelCase  :	int								=	{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}







						def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		List[str]=True ,				lowerCamelCase_			:		List[Any]=None ,				lowerCamelCase_			:		Tuple=3 ,				lowerCamelCase_			:		List[str]=3_00 ,				lowerCamelCase_			:		int=6 ,				lowerCamelCase_			:		Optional[Any]=20_48 ,				lowerCamelCase_			:		Any=8 ,				lowerCamelCase_			:		Tuple=6 ,				lowerCamelCase_			:		List[str]=20_48 ,				lowerCamelCase_			:		Dict=8 ,				lowerCamelCase_			:		str=0.0 ,				lowerCamelCase_			:		Optional[int]=0.0 ,				lowerCamelCase_			:		List[str]=True ,				lowerCamelCase_			:		Optional[int]="relu" ,				lowerCamelCase_			:		Dict=2_56 ,				lowerCamelCase_			:		Any=0.1 ,				lowerCamelCase_			:		Union[str, Any]=0.0 ,				lowerCamelCase_			:		str=0.0 ,				lowerCamelCase_			:		List[Any]=0.02 ,				lowerCamelCase_			:		Union[str, Any]=1.0 ,				lowerCamelCase_			:		Union[str, Any]=False ,				lowerCamelCase_			:		str="sine" ,				lowerCamelCase_			:		Dict="resnet50" ,				lowerCamelCase_			:		Dict=True ,				lowerCamelCase_			:		str=False ,				lowerCamelCase_			:		Union[str, Any]=2 ,				lowerCamelCase_			:		Tuple=5 ,				lowerCamelCase_			:		Any=2 ,				lowerCamelCase_			:		Union[str, Any]=1 ,				lowerCamelCase_			:		str=1 ,				lowerCamelCase_			:		Tuple=2 ,				lowerCamelCase_			:		Optional[Any]=5 ,				lowerCamelCase_			:		Optional[int]=2 ,				lowerCamelCase_			:		str=0.25 ,				**lowerCamelCase_			:		Dict ,				):





										'''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.'					)
																		_snake_case :			str  						=   CONFIG_MAPPING['resnet'](out_features=['stage4']					)
														elif isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
																		_snake_case :			List[Any]  						=   backbone_config.get('model_type'					)
																		_snake_case :			str  						=   CONFIG_MAPPING[backbone_model_type]
																		_snake_case :			str  						=   config_class.from_dict(lowerCamelCase_					)

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







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										return self.encoder_attention_heads







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										return self.d_model







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







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



class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Optional[int]								=	version.parse("1.11"     )







						@property
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







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







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





										'''simple docstring'''







										return 1e-5







						@property
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										return 12













import math

def       A__(		__lowerCAmelCase ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,			int(math.sqrt(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


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






