




'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase_							:	Optional[Any]	     =		{
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	Union[str, Any]	     =		['''MobileBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileBertForMaskedLM''',
         '''MobileBertForMultipleChoice''',
         '''MobileBertForNextSentencePrediction''',
         '''MobileBertForPreTraining''',
         '''MobileBertForQuestionAnswering''',
         '''MobileBertForSequenceClassification''',
         '''MobileBertForTokenClassification''',
         '''MobileBertLayer''',
         '''MobileBertModel''',
         '''MobileBertPreTrainedModel''',
         '''load_tf_weights_in_mobilebert''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	Tuple	     =		[
         '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFMobileBertForMaskedLM''',
         '''TFMobileBertForMultipleChoice''',
         '''TFMobileBertForNextSentencePrediction''',
         '''TFMobileBertForPreTraining''',
         '''TFMobileBertForQuestionAnswering''',
         '''TFMobileBertForSequenceClassification''',
         '''TFMobileBertForTokenClassification''',
         '''TFMobileBertMainLayer''',
         '''TFMobileBertModel''',
         '''TFMobileBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_							:	int	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










import math




def  a   (lowerCAmelCase__   ):

   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  a   (lowerCAmelCase__ = 10_001   ):

   try:
      __a					=  int(lowerCAmelCase__   )
   except (TypeError, ValueError):
      raise TypeError("""Parameter nth must be int or castable to int."""   ) from None
   if nth <= 0:
      raise ValueError("""Parameter nth must be greater than or equal to one."""   )
   __a					=  []
   __a					=  2
   while len(lowerCAmelCase__   ) < nth:
      if is_prime(lowerCAmelCase__   ):
         primes.append(lowerCAmelCase__   )
         num += 1
      else:
         num += 1
   return primes[len(lowerCAmelCase__   ) - 1]


if __name__ == "__main__":
  print(f'''{solution() = }''')



