




'''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()










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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




