



'''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 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 pass







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


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







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




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







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




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      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_       		=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_)

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

      a_       		=np.random.RandomState(0)
      a_       		=pipe(
          prompt=lowerCAmelCase_							,  image=lowerCAmelCase_							,  mask_image=lowerCAmelCase_							,  guidance_scale=7.5							,  num_inference_steps=1_0							,  generator=lowerCAmelCase_							,  output_type="np"							,  )
      a_       		=output.images
      a_       		=images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4])

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




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      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_       		=LMSDiscreteScheduler.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,  subfolder="scheduler"							,  revision="onnx")
      a_       		=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_)

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

      a_       		=np.random.RandomState(0)
      a_       		=pipe(
          prompt=lowerCAmelCase_							,  image=lowerCAmelCase_							,  mask_image=lowerCAmelCase_							,  guidance_scale=7.5							,  num_inference_steps=2_0							,  generator=lowerCAmelCase_							,  output_type="np"							,  )
      a_       		=output.images
      a_       		=images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5])

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







'''simple docstring'''


from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip


