

class     __magic_name__     :


 def __init__(    self		:			List[Any]     )     ->   Any:
   UpperCAmelCase          =	[0, 1]


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			int     )     ->   List[str]:





   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   _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          =	Fibonacci()

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

    try:
      UpperCAmelCase          =	int(_UpperCAmelCase      )
    except ValueError:
      print("Enter a number or \'exit\'"      )
      continue

    print(fibonacci.get(_UpperCAmelCase      )      )


if __name__ == "__main__":
      main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__A       ={
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2Text2ForCausalLM",
				    "Speech2Text2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
				from .processing_speech_to_text_a import SpeechaTextaProcessor
				from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text_a import (
								    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextaForCausalLM,
								    SpeechaTextaPreTrainedModel,
								)

else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





