



from __future__ import annotations

import math




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  bool:



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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Dict										=       str(_lowerCAmelCase		)
    A			:	List[Any]										=       [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      __UpperCamelCase     (  _lowerCAmelCase		)	->  bool:



    """simple docstring"""






    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      __UpperCamelCase     (  _lowerCAmelCase = 11		)	->  list[int]:



    """simple docstring"""






    A			:	list[int]										=       []
    A			:	Any										=       13
    while len(_lowerCAmelCase		) != count:
        if validate(_lowerCAmelCase		):
            A			:	str										=       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      __UpperCamelCase     (  )	->  int:



    """simple docstring"""






    return sum(compute_truncated_primes(11		)		)


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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


