


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """table-transformer"""
     _lowerCamelCase   =      ["""past_key_values"""]
     _lowerCamelCase   =      {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }


     def __init__(	self  ,       __A=True  ,       __A=None  ,       __A=3  ,       __A=100  ,       __A=6  ,       __A=2048  ,       __A=8  ,       __A=6  ,       __A=2048  ,       __A=8  ,       __A=0.0  ,       __A=0.0  ,       __A=True  ,       __A="relu"  ,       __A=256  ,       __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  ,       ):
        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					=  CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
           elif isinstance(__A  ,       __A    ):
              __a					=  backbone_config.get("""model_type"""    )
              __a					=  CONFIG_MAPPING[backbone_model_type]
              __a					=  config_class.from_dict(__A    )
           # set timm attributes to None
           __a	,    __a	,    __a					=  None, None, None

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


     @property
     def 	snake_case_  (	self    ):
        return self.encoder_attention_heads


     @property
     def 	snake_case_  (	self    ):
        return self.d_model







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      version.parse("""1.11"""							)


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


     @property
     def 	snake_case_  (	self    ):
        return 1E-5


     @property
     def 	snake_case_  (	self    ):
        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__ = 10_001   ):

   try:
      __a					=  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."""   )
   __a					=  []
   __a					=  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() = }''')



