



class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      [0, 1]




			def 					snake_case__	(  self				:					int			,			a_				:					int					):
					'''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		( ):







		'''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=''''''					,  )
		__UpperCAmelCase       :	str						=      Fibonacci()

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

				try:
						__UpperCAmelCase       :	int						=      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__)





