



'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: int    = 'deta'
 __lowerCamelCase					: Union[str, Any]    = {
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }



 def __init__(self				,     A=None				,     A=900				,     A=2_048				,     A=6				,     A=2_048				,     A=8				,     A=6				,     A=1_024				,     A=8				,     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=True				,     A=False				,     A="sine"				,     A=5				,     A=4				,     A=4				,     A=True				,     A=300				,     A=True				,     A=True				,     A=1				,     A=5				,     A=2				,     A=1				,     A=1				,     A=5				,     A=2				,     A=0.1				,     A=0.25				,     **A				,     )					->  Optional[int]:


      """simple docstring"""



      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=['''stage2''', '''stage3''', '''stage4'''] )
      else:
           if isinstance(A				,     A ):
                _a											=						backbone_config.pop('''model_type''' )
                _a											=						CONFIG_MAPPING[backbone_model_type]
                _a											=						config_class.from_dict(A )

      _a											=						backbone_config
      _a											=						num_queries
      _a											=						max_position_embeddings
      _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											=						auxiliary_loss
      _a											=						position_embedding_type
      # deformable attributes
      _a											=						num_feature_levels
      _a											=						encoder_n_points
      _a											=						decoder_n_points
      _a											=						two_stage
      _a											=						two_stage_num_proposals
      _a											=						with_box_refine
      _a											=						assign_first_stage
      if two_stage is True and with_box_refine is False:
           raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
      # 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
      _a											=						focal_alpha
      super().__init__(is_encoder_decoder=A				,     **A )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						copy.deepcopy(self.__dict__ )
      _a											=						self.backbone_config.to_dict()
      _a											=						self.__class__.model_type
      return output








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


if __name__ == "__main__":
  print(F"""{len(find_circular_primes()) = }""")



