





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42


if is_transformers_available() and is_torch_available():
   from .modeling_roberta_series import RobertaSeriesModelWithTransformation
   from .pipeline_alt_diffusion import AltDiffusionPipeline
   from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

