





'''simple docstring'''







import math



class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : Optional[Any]   ,UpperCamelCase    : List[str]   ,UpperCamelCase    : Tuple   ,UpperCamelCase    : Tuple  )		->  List[Any]:
           _lowercase							:      Tuple  						=							x
           _lowercase							:      str  						=							y
           _lowercase							:      List[Any]  						=							z



      def __repr__(							self    : List[str]  )		->  str:
           return F'''Point({self.x}, {self.y}, {self.z})'''

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Point       ,  lowerCAmelCase__     :	Point)			->						float:

     '''simple docstring'''




     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''simple docstring'''







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 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Union[List[PIL.Image.Image], np.ndarray]
      lowerCAmelCase__       :      Optional[List[bool]]


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