




'''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 ):







   @property
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )







   @property
   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					ort.SessionOptions()
     __a			=					False
     return options







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'      )
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'      )
     __a			=					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
     __a			=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4'      ,							revision='onnx'      ,							safety_checker=UpperCamelCase      ,							feature_extractor=UpperCamelCase      ,							provider=self.gpu_provider      ,							sess_options=self.gpu_options      ,							)
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					'A red cat sitting on a park bench'

     __a			=					np.random.RandomState(0      )
     __a			=					pipe(
         prompt=UpperCamelCase      ,							image=UpperCamelCase      ,							mask_image=UpperCamelCase      ,							strength=0.75      ,							guidance_scale=7.5      ,							num_inference_steps=15      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)

     __a			=					output.images[0]

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









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



