




"""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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					42


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 					lowercase(     __a ):







      '''simple docstring'''





      lowercase__ 					=					42
      lowercase__ 					=					42







     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"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""		)

   # using the PNDM scheduler by default
   _snake_case							:		Optional[Any]								=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""onnx""",      safety_checker=a_,      feature_extractor=a_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image		).max() < 1E-2


