






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   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      Optional[Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
      @property
      def 	UpperCAmelCase      (      self					:			Any					)							->      Any:
        __UpperCAmelCase							:	List[str]           =  ort.SessionOptions()
        __UpperCAmelCase							:	List[str]           =  False
        return options




      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        __UpperCAmelCase							:	Any           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo.png"""					)
        __UpperCAmelCase							:	List[str]           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""					)
        __UpperCAmelCase							:	Optional[int]           =  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
        __UpperCAmelCase							:	Optional[int]           =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
            """CompVis/stable-diffusion-v1-4"""		,      revision="""onnx"""		,      safety_checker=__lowercase		,      feature_extractor=__lowercase		,      provider=self.gpu_provider		,      sess_options=self.gpu_options		,      )
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        __UpperCAmelCase							:	List[str]           =  np.random.RandomState(0					)
        __UpperCAmelCase							:	Union[str, Any]           =  pipe(
            prompt=__lowercase		,      image=__lowercase		,      mask_image=__lowercase		,      strength=0.75		,      guidance_scale=7.5		,      num_inference_steps=15		,      generator=__lowercase		,      output_type="""np"""		,      )

        __UpperCAmelCase							:	List[Any]           =  output.images[0]

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










def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


