





'''simple docstring'''




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__   :				Optional[int]      =       logging.get_logger(__name__)

lowercase__   :				Union[str, Any]      =       {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''table-transformer'''
     lowerCAmelCase		      =	['''past_key_values''']
     lowerCAmelCase		      =	{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(						self						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     _UpperCAmelCase=3						,     _UpperCAmelCase=100						,     _UpperCAmelCase=6						,     _UpperCAmelCase=2048						,     _UpperCAmelCase=8						,     _UpperCAmelCase=6						,     _UpperCAmelCase=2048						,     _UpperCAmelCase=8						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=True						,     _UpperCAmelCase="relu"						,     _UpperCAmelCase=256						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1.0						,     _UpperCAmelCase=False						,     _UpperCAmelCase="sine"						,     _UpperCAmelCase="resnet50"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=False						,     _UpperCAmelCase=1						,     _UpperCAmelCase=5						,     _UpperCAmelCase=2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=1						,     _UpperCAmelCase=5						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.1						,     **_UpperCAmelCase						,     ):

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

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


     @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




class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	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 12






'''simple docstring'''




import itertools
import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					bool:

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






def 	_lowerCAmelCase      (  )					->					List[Any]:
     __A						:			Optional[Any]								=	2
     while True:
          if is_prime(__snake_case      ):
               yield num
          num += 1






def 	_lowerCAmelCase      (  __snake_case  :			int = 1_00_01      )					->					int:
     return next(itertools.islice(prime_generator()			,							nth - 1			,							__snake_case      )      )


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