




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Tuple				=       {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      str				=       [
          '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''QDQBertForMaskedLM''',
          '''QDQBertForMultipleChoice''',
          '''QDQBertForNextSentencePrediction''',
          '''QDQBertForQuestionAnswering''',
          '''QDQBertForSequenceClassification''',
          '''QDQBertForTokenClassification''',
          '''QDQBertLayer''',
          '''QDQBertLMHeadModel''',
          '''QDQBertModel''',
          '''QDQBertPreTrainedModel''',
          '''load_tf_weights_in_qdqbert''',
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      lowercase	:      List[Any]				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [0, 1]





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if (difference := index - (len(self.sequence ) - 2)) >= 1:
               for _ in range(A ):
                      self.sequence.append(self.sequence[-1] + self.sequence[-2] )
        return self.sequence[:index]






def 		a_( )				->None:






       '''simple docstring'''




       print(
           '''Fibonacci Series Using Dynamic Programming\n'''   ,				'''Enter the index of the Fibonacci number you want to calculate '''   ,				'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''   ,				sep=''''''   ,				)
       lowercase  :		Optional[int]						=      Fibonacci()

       while True:
              lowercase  :		str						=      input('''>> '''      )
              if prompt in {"exit", "quit"}:
                     break

              try:
                     lowercase  :		int						=      int(lowerCamelCase      )
              except ValueError:
                     print('''Enter a number or \'exit\''''      )
                     continue

              print(fibonacci.get(lowerCamelCase      )      )


if __name__ == "__main__":
      main()




