

from __future__ import annotations

import math





def      A__					(			__A  :						float     ,				__A  :						int			)					->float:
      __A    =u
      for i in range(1     ,				__A			):
            __A    =temp * (u - i)
      return temp





def      A__					(			)					->None:
      __A    =int(input('''enter the numbers of values: '''			)			)
      __A    =[]
      for _ in range(__A			):
            y.append([]			)
      for i in range(__A			):
            for j in range(__A			):
                  y[i].append(__A			)
                  __A    =0

      print('''enter the values of parameters in a list: '''			)
      __A    =list(map(__A     ,				input().split()			)			)

      print('''enter the values of corresponding parameters: '''			)
      for i in range(__A			):
            __A    =float(input()			)

      __A    =int(input('''enter the value to interpolate: '''			)			)
      __A    =(value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1     ,				__A			):
            for j in range(n - i			):
                  __A    =y[j + 1][i - 1] - y[j][i - 1]

      __A    =y[0][0]
      for i in range(1     ,				__A			):
            summ += (ucal(__A     ,				__A			) * y[0][i]) / math.factorial(__A			)

      print(F'''the value at {value} is {summ}'''			)


if __name__ == "__main__":
   main()





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


