

'''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 							A__   (	unittest.TestCase  ):





  @property
  def 						A  (		self				:     Optional[int]			)     ->   str:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Any			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ort.SessionOptions()
       _SCREAMING_SNAKE_CASE							   =False
       return options





  def 						A  (		self				:     Any			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'			)
       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'			)
       _SCREAMING_SNAKE_CASE							   =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
       _SCREAMING_SNAKE_CASE							   =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			)

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

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

       _SCREAMING_SNAKE_CASE							   =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 							A__   (	metaclass=A__  ):
  A__					=    ['note_seq']





  def __init__(		self				:     List[str]							, *_a				:     Any							, **_a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       requires_backends(self							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     Any							, *_a				:     str							, **_a				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     int							, *_a				:     Optional[Any]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)
