






'''simple docstring'''
from __future__ import annotations

import math




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[str]							     =  str(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  [n]
   for i in range(1    ,		len(_UpperCamelCase				)				):
      list_nums.append(int(str_num[i:]				)				)
      list_nums.append(int(str_num[:-i]				)				)
   return list_nums




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   if len(str(_UpperCamelCase				)				) > 3:
      if not is_prime(int(str(_UpperCamelCase				)[-3:]				)				) or not is_prime(int(str(_UpperCamelCase				)[:3]				)				):
         return False
   return True




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase = 11				):


   """simple docstring"""




   lowercase_						:     list[int]							     =  []
   lowercase_						:     Optional[Any]							     =  13
   while len(_UpperCamelCase				) != count:
      if validate(_UpperCamelCase				):
         lowercase_						:     Union[str, Any]							     =  list_truncated_nums(_UpperCamelCase				)
         if all(is_prime(_UpperCamelCase				) for i in list_nums				):
            list_truncated_primes.append(_UpperCamelCase				)
      num += 2
   return list_truncated_primes




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
 print(f"""{sum(compute_truncated_primes(11)) = }""")












'''simple docstring'''
import copy

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


UpperCamelCase__			=      logging.get_logger(__name__)

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


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Union[str, Any]									=     'deta'
    __lowerCamelCase: str									=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }




    def __init__(      self	:							List[str]	,  a	:							Union[str, Any]=None	,  a	:							Dict=9_0_0	,  a	:							str=2_0_4_8	,  a	:							Union[str, Any]=6	,  a	:							int=2_0_4_8	,  a	:							Optional[int]=8	,  a	:							Optional[Any]=6	,  a	:							Optional[Any]=1_0_2_4	,  a	:							Any=8	,  a	:							Dict=0.0	,  a	:							Optional[int]=True	,  a	:							Union[str, Any]="relu"	,  a	:							str=2_5_6	,  a	:							str=0.1	,  a	:							Any=0.0	,  a	:							Optional[Any]=0.0	,  a	:							str=0.02	,  a	:							int=1.0	,  a	:							Tuple=True	,  a	:							Optional[int]=False	,  a	:							str="sine"	,  a	:							List[Any]=5	,  a	:							str=4	,  a	:							Any=4	,  a	:							Optional[Any]=True	,  a	:							Union[str, Any]=3_0_0	,  a	:							List[Any]=True	,  a	:							Tuple=True	,  a	:							Any=1	,  a	:							int=5	,  a	:							Optional[int]=2	,  a	:							Tuple=1	,  a	:							List[str]=1	,  a	:							Tuple=5	,  a	:							Tuple=2	,  a	:							List[str]=0.1	,  a	:							Tuple=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."      )
          lowercase_						:     Optional[int]							     =  CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
       else:
          if isinstance(a	,  a      ):
             lowercase_						:     Any							     =  backbone_config.pop("model_type"      )
             lowercase_						:     str							     =  CONFIG_MAPPING[backbone_model_type]
             lowercase_						:     Any							     =  config_class.from_dict(a      )

       lowercase_						:     Optional[int]							     =  backbone_config
       lowercase_						:     Dict							     =  num_queries
       lowercase_						:     Tuple							     =  max_position_embeddings
       lowercase_						:     Union[str, Any]							     =  d_model
       lowercase_						:     Optional[Any]							     =  encoder_ffn_dim
       lowercase_						:     Any							     =  encoder_layers
       lowercase_						:     int							     =  encoder_attention_heads
       lowercase_						:     Tuple							     =  decoder_ffn_dim
       lowercase_						:     Dict							     =  decoder_layers
       lowercase_						:     Dict							     =  decoder_attention_heads
       lowercase_						:     List[Any]							     =  dropout
       lowercase_						:     Union[str, Any]							     =  attention_dropout
       lowercase_						:     List[str]							     =  activation_dropout
       lowercase_						:     Optional[Any]							     =  activation_function
       lowercase_						:     str							     =  init_std
       lowercase_						:     Dict							     =  init_xavier_std
       lowercase_						:     List[Any]							     =  encoder_layerdrop
       lowercase_						:     Optional[int]							     =  auxiliary_loss
       lowercase_						:     str							     =  position_embedding_type
       # deformable attributes
       lowercase_						:     Optional[int]							     =  num_feature_levels
       lowercase_						:     List[Any]							     =  encoder_n_points
       lowercase_						:     Optional[Any]							     =  decoder_n_points
       lowercase_						:     List[Any]							     =  two_stage
       lowercase_						:     Any							     =  two_stage_num_proposals
       lowercase_						:     List[str]							     =  with_box_refine
       lowercase_						:     Optional[int]							     =  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
       lowercase_						:     int							     =  class_cost
       lowercase_						:     Optional[int]							     =  bbox_cost
       lowercase_						:     str							     =  giou_cost
       # Loss coefficients
       lowercase_						:     Optional[int]							     =  mask_loss_coefficient
       lowercase_						:     Union[str, Any]							     =  dice_loss_coefficient
       lowercase_						:     Optional[Any]							     =  bbox_loss_coefficient
       lowercase_						:     Optional[Any]							     =  giou_loss_coefficient
       lowercase_						:     Any							     =  eos_coefficient
       lowercase_						:     str							     =  focal_alpha
       super().__init__(is_encoder_decoder=a	,  **a      )




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.encoder_attention_heads




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.d_model





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  copy.deepcopy(self.__dict__      )
       lowercase_						:     Union[str, Any]							     =  self.backbone_config.to_dict()
       lowercase_						:     str							     =  self.__class__.model_type
       return output




