

"""simple docstring"""




from typing import TYPE_CHECKING

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


A_							:     Union[str, Any]     =	{
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_							:     Tuple     =	["ImageGPTFeatureExtractor"]
      A_							:     str     =	["ImageGPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_							:     List[str]     =	[
          "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ImageGPTForCausalImageModeling",
          "ImageGPTForImageClassification",
          "ImageGPTModel",
          "ImageGPTPreTrainedModel",
          "load_tf_weights_in_imagegpt",
      ]


if TYPE_CHECKING:
      from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
            from .image_processing_imagegpt import ImageGPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_imagegpt import (
                IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ImageGPTForCausalImageModeling,
                ImageGPTForImageClassification,
                ImageGPTModel,
                ImageGPTPreTrainedModel,
                load_tf_weights_in_imagegpt,
            )

else:
      import sys

      A_							:     str     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												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=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

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

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

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


