



"""simple docstring"""
from __future__ import annotations

import math

def       _snake_case						(						UpperCamelCase						:		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(math.sqrt(UpperCamelCase						) + 1						)			,		6						):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[Any]	=	str(UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	[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       _snake_case						(						UpperCamelCase						:		int						):
 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       _snake_case						(						UpperCamelCase						:		int = 11						):
 UpperCAmelCase	:					list[int]	=	[]
 UpperCAmelCase	:					str	=	13
 while len(UpperCamelCase						) != count:
  if validate(UpperCamelCase						):
   UpperCAmelCase	:					Tuple	=	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       _snake_case						(						):
 return sum(compute_truncated_primes(11						)						)


if __name__ == "__main__":
      print(f"""{sum(compute_truncated_primes(1_1)) = }""")





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
