




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__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """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     a__					(      snake_case__   ):
						_a							:					str      =					"""detr"""
						_a							:					Union[str, Any]      =					["""past_key_values"""]
						_a							:					Union[str, Any]      =					{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}






						def __init__(     self	,    _A=True	,    _A=None	,    _A=3	,    _A=1_0_0	,    _A=6	,    _A=2_0_4_8	,    _A=8	,    _A=6	,    _A=2_0_4_8	,    _A=8	,    _A=0.0	,    _A=0.0	,    _A=True	,    _A="relu"	,    _A=2_5_6	,    _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	,    ):




											"""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."    )
																					__lowerCAmelCase            = CONFIG_MAPPING["resnet"](out_features=["stage4"]    )
																elif isinstance(_A	,    _A    ):
																					__lowerCAmelCase            = backbone_config.get("model_type"    )
																					__lowerCAmelCase            = CONFIG_MAPPING[backbone_model_type]
																					__lowerCAmelCase            = config_class.from_dict(_A    )
																# set timm attributes to None
																__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = None, None, None

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






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.encoder_attention_heads






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.d_model






						@classmethod
						def        __SCREAMING_SNAKE_CASE(     cls	,    _A	,    **_A    ):




											"""simple docstring"""



											return cls(backbone_config=_A	,    **_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = copy.deepcopy(self.__dict__    )
											if output["backbone_config"] is not None:
																__lowerCAmelCase            = self.backbone_config.to_dict()
											__lowerCAmelCase            = self.__class__.model_type
											return output






class     a__					(      snake_case__   ):
						_a							:					List[Any]      =					version.parse("""1.11"""   )






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return 1E-5






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return 1_2










from math import sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_01  ):
					__lowerCAmelCase            = 0
					__lowerCAmelCase            = 1
					while count != nth and number < 3:
										number += 1
										if is_prime(SCREAMING_SNAKE_CASE_  ):
															count += 1
					while count != nth:
										number += 2
										if is_prime(SCREAMING_SNAKE_CASE_  ):
															count += 1
					return number


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




