






class 					a					:



      """simple docstring"""


      def __init__(   self      :    Any      )							->   None:
        __UpperCAmelCase							:			Union[str, Any]     =						[0, 1]




      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    int      )							->   list:
        if (difference := index - (len(self.sequence      ) - 2)) >= 1:
          for _ in range(__lowercase      ):
            self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
        return self.sequence[:index]






def        lowerCamelCase__   (					):
  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=""""""						,	)
  __UpperCAmelCase							:			int     =						Fibonacci()

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

    try:
      __UpperCAmelCase							:			int     =						int(__lowerCamelCase	)
    except ValueError:
      print("""Enter a number or 'exit'"""	)
      continue

    print(fibonacci.get(__lowerCamelCase	)	)


if __name__ == "__main__":
       main()










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

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


