



'''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    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




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




  @property
  def 		_lowercase	(					self :      Any  )   ->   Optional[Any]:
    __lowerCamelCase			:			List[Any]       			=					ort.SessionOptions()
    __lowerCamelCase			:			List[str]       			=					False
    return options




  def 		_lowercase	(					self :      List[str]  )   ->   Dict:
    __lowerCamelCase			:			Union[str, Any]       			=					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/in_paint/overture-creations-5sI6fQgYIuo.png'  )
    __lowerCamelCase			:			Optional[int]       			=					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'  )
    __lowerCamelCase			:			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
    __lowerCamelCase			:			List[str]       			=					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  )

    __lowerCamelCase			:			Optional[Any]       			=					'A red cat sitting on a park bench'

    __lowerCamelCase			:			Union[str, Any]       			=					np.random.RandomState(0  )
    __lowerCamelCase			:			Any       			=					pipe(
        prompt=_a     ,      image=_a     ,      mask_image=_a     ,      strength=0.75     ,      guidance_scale=7.5     ,      num_inference_steps=15     ,      generator=_a     ,      output_type='np'     ,      )

    __lowerCamelCase			:			Any       			=					output.images[0]

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











'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






