

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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				"detr"
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["past_key_values"]
			SCREAMING_SNAKE_CASE     :   Dict									=				{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}







			def __init__(     self :				List[Any]							,   _UpperCamelCase :				Tuple=True							,   _UpperCamelCase :				str=None							,   _UpperCamelCase :				Union[str, Any]=3							,   _UpperCamelCase :				Optional[Any]=1_0_0							,   _UpperCamelCase :				Any=6							,   _UpperCamelCase :				Tuple=2_0_4_8							,   _UpperCamelCase :				Any=8							,   _UpperCamelCase :				List[Any]=6							,   _UpperCamelCase :				Dict=2_0_4_8							,   _UpperCamelCase :				Tuple=8							,   _UpperCamelCase :				List[str]=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				str=True							,   _UpperCamelCase :				str="relu"							,   _UpperCamelCase :				Optional[int]=2_5_6							,   _UpperCamelCase :				str=0.1							,   _UpperCamelCase :				Tuple=0.0							,   _UpperCamelCase :				Any=0.0							,   _UpperCamelCase :				str=0.02							,   _UpperCamelCase :				Optional[Any]=1.0							,   _UpperCamelCase :				int=False							,   _UpperCamelCase :				str="sine"							,   _UpperCamelCase :				Union[str, Any]="resnet50"							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				int=False							,   _UpperCamelCase :				List[Any]=1							,   _UpperCamelCase :				Optional[int]=5							,   _UpperCamelCase :				Optional[Any]=2							,   _UpperCamelCase :				str=1							,   _UpperCamelCase :				str=1							,   _UpperCamelCase :				Optional[int]=5							,   _UpperCamelCase :				Tuple=2							,   _UpperCamelCase :				str=0.1							,   **_UpperCamelCase :				Any							,   )      ->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.'''							)
																					snake_case_   =	CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
															elif isinstance(_UpperCamelCase							,   _UpperCamelCase							):
																					snake_case_   =	backbone_config.get('''model_type'''							)
																					snake_case_   =	CONFIG_MAPPING[backbone_model_type]
																					snake_case_   =	config_class.from_dict(_UpperCamelCase							)
															# 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=_UpperCamelCase							,   **_UpperCamelCase							)







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







			@property
			def 				snake_case__(     self :				List[str]							)      ->int:
									return self.d_model







			@classmethod
			def 				snake_case__(     cls :				List[str]							,   _UpperCamelCase :				PretrainedConfig							,   **_UpperCamelCase :				Union[str, Any]							)      ->Optional[int]:
									return cls(backbone_config=_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Any							)      ->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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				version.parse("1.11"					)







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







			@property
			def 				snake_case__(     self :				Optional[Any]							)      ->float:
									return 1e-5







			@property
			def 				snake_case__(     self :				List[str]							)      ->int:
									return 1_2


import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						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(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						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_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


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