


'''simple docstring'''


from __future__ import annotations

import math




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




def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Optional[int]          =				str(lowerCamelCase)
      A_						:  Optional[int]          =				[n]
      for i in range(1							,						len(lowerCamelCase)):
            list_nums.append(int(str_num[i:]))
            list_nums.append(int(str_num[:-i]))
      return list_nums




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




def      lowerCamelCase					(      lowerCamelCase  :      int = 11):
      A_						:  list[int]          =				[]
      A_						:  Union[str, Any]          =				13
      while len(lowerCamelCase) != count:
            if validate(lowerCamelCase):
                  A_						:  Dict          =				list_truncated_nums(lowerCamelCase)
                  if all(is_prime(lowerCamelCase) for i in list_nums):
                        list_truncated_primes.append(lowerCamelCase)
            num += 2
      return list_truncated_primes




def      lowerCamelCase					(      ):
      return sum(compute_truncated_primes(11))


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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
