"""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,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)

@dataclass
class SCREAMING_SNAKE_CASE  (     a_						):







 """simple docstring"""


 a_							:						Optional[int]	      =4_2
 a_							:						int	      =4_2


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipeline_cycle_diffusion import CycleDiffusionPipeline
     from .pipeline_stable_diffusion import StableDiffusionPipeline
     from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
     from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
     from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
     from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
     from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
     from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
     from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
     from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
     from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
     from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
     from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
     from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
     from .pipeline_stable_unclip import StableUnCLIPPipeline
     from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
     from .safety_checker import StableDiffusionSafetyChecker
     from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

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 StableDiffusionImageVariationPipeline
else:
     from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionPixaPixZeroPipeline,
     )
else:
     from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
     from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
     from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
     if not (
         is_torch_available()
         and is_transformers_available()
         and is_k_diffusion_available()
         and is_k_diffusion_version(">=", "0.0.12")
     ):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
     if not (is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
     from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
     from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
     from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
     from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
     import flax

     @flax.struct.dataclass
     class SCREAMING_SNAKE_CASE  (     a_						):







      """simple docstring"""


      a_							:						int	      =4_2
      a_							:						str	      =4_2
     from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
     from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
     from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
     from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
     from .safety_checker_flax import FlaxStableDiffusionSafetyChecker




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


