




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      ):

     '''simple docstring'''





     pass



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

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
     @property
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					ort.SessionOptions()
         UpperCAmelCase_										=					False
         return options
     def   lowerCamelCase_       (     self       :      List[str]    ):
         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=__snake_case ,   feature_extractor=__snake_case ,   provider=self.gpu_provider ,   sess_options=self.gpu_options ,   )
         pipe.set_progress_bar_config(disable=__snake_case    )

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

         UpperCAmelCase_										=					np.random.RandomState(0    )
         UpperCAmelCase_										=					pipe(
             prompt=__snake_case ,   image=__snake_case ,   mask_image=__snake_case ,   guidance_scale=7.5 ,   num_inference_steps=10 ,   generator=__snake_case ,   output_type='''np''' ,   )
         UpperCAmelCase_										=					output.images
         UpperCAmelCase_										=					images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         UpperCAmelCase_										=					np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]    )

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




     def   lowerCamelCase_       (     self       :      Union[str, 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_										=					LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-inpainting''' ,   subfolder='''scheduler''' ,   revision='''onnx'''    )
         UpperCAmelCase_										=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
             '''runwayml/stable-diffusion-inpainting''' ,   revision='''onnx''' ,   scheduler=__snake_case ,   safety_checker=__snake_case ,   feature_extractor=__snake_case ,   provider=self.gpu_provider ,   sess_options=self.gpu_options ,   )
         pipe.set_progress_bar_config(disable=__snake_case    )

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

         UpperCAmelCase_										=					np.random.RandomState(0    )
         UpperCAmelCase_										=					pipe(
             prompt=__snake_case ,   image=__snake_case ,   mask_image=__snake_case ,   guidance_scale=7.5 ,   num_inference_steps=20 ,   generator=__snake_case ,   output_type='''np''' ,   )
         UpperCAmelCase_										=					output.images
         UpperCAmelCase_										=					images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         UpperCAmelCase_										=					np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]    )

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











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





