'''simple docstring'''





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort




class        __A      ( A_		,	unittest.TestCase	):
    # FIXME: add fast tests
    pass




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



    @property
    def 					_lowercase						(self    :    int ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )



    @property
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   ort.SessionOptions()
         UpperCAmelCase_           =   False
         return options



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png" )
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
         UpperCAmelCase_           =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"   ,			revision="onnx"   ,			safety_checker=_lowerCamelCase   ,			feature_extractor=_lowerCamelCase   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         pipe.set_progress_bar_config(disable=_lowerCamelCase )

         UpperCAmelCase_           =   "A red cat sitting on a park bench"

         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   pipe(
             prompt=_lowerCamelCase   ,			image=_lowerCamelCase   ,			mask_image=_lowerCamelCase   ,			guidance_scale=7.5   ,			num_inference_steps=10   ,			generator=_lowerCamelCase   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png" )
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
         UpperCAmelCase_           =   LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-inpainting"   ,			subfolder="scheduler"   ,			revision="onnx" )
         UpperCAmelCase_           =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"   ,			revision="onnx"   ,			scheduler=_lowerCamelCase   ,			safety_checker=_lowerCamelCase   ,			feature_extractor=_lowerCamelCase   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         pipe.set_progress_bar_config(disable=_lowerCamelCase )

         UpperCAmelCase_           =   "A red cat sitting on a park bench"

         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   pipe(
             prompt=_lowerCamelCase   ,			image=_lowerCamelCase   ,			mask_image=_lowerCamelCase   ,			guidance_scale=7.5   ,			num_inference_steps=20   ,			generator=_lowerCamelCase   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






"""simple docstring"""



UpperCAmelCase__          = {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}



