






'''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      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





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


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      A     (   SCREAMING_SNAKE_CASE__    ):






       """simple docstring"""





       __a      :    np.ndarray
       __a      :    List[bool]






      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 collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






