


'''simple docstring'''







from typing import TYPE_CHECKING

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


A   :  Any       		=				{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A   :  Any       		=				['''MPNetTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A   :  str       		=				[
       '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MPNetForMaskedLM''',
       '''MPNetForMultipleChoice''',
       '''MPNetForQuestionAnswering''',
       '''MPNetForSequenceClassification''',
       '''MPNetForTokenClassification''',
       '''MPNetLayer''',
       '''MPNetModel''',
       '''MPNetPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A   :  Union[str, Any]       		=				[
       '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFMPNetEmbeddings''',
       '''TFMPNetForMaskedLM''',
       '''TFMPNetForMultipleChoice''',
       '''TFMPNetForQuestionAnswering''',
       '''TFMPNetForSequenceClassification''',
       '''TFMPNetForTokenClassification''',
       '''TFMPNetMainLayer''',
       '''TFMPNetModel''',
       '''TFMPNetPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
   from .tokenization_mpnet import MPNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mpnet_fast import MPNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mpnet import (
          MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetLayer,
          MPNetModel,
          MPNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mpnet import (
          TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMPNetEmbeddings,
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetMainLayer,
          TFMPNetModel,
          TFMPNetPreTrainedModel,
      )

else:
   import sys

   A   :  Union[str, Any]       		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''







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 = 1000						,lowerCamelCase  :    int = 1000     ):
       _A  :			int											=      [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1						,lowerCamelCase     ):
              for b in range(2						,lowerCamelCase     ):
                     if is_prime(lowerCamelCase     ):
                            _A  :			Dict											=      0
                            _A  :			Tuple											=      0
                            while is_prime((n**2) + (a * n) + b     ):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   _A  :			List[str]											=      [count, a, b]
       _A  :			str											=      longest[1] * longest[2]
       return ans


if __name__ == "__main__":
   print(solution(1000, 1000))






