



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 	lowerCamelCase__	(       _a							,  unittest.TestCase):



 """simple docstring"""

 pass


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



 """simple docstring"""

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )

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

 def     snake_case_      (    self					:					int      )   ->	Dict:
    _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=10			,						generator=lowerCamelCase__			,						output_type='''np'''			,						)
    _A      =       output.images
    _A      =       images[0, 2_55:2_58, 2_55:2_58, -1]

    assert images.shape == (1, 5_12, 5_12, 3)
    _A      =       np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]      )

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

 def     snake_case_      (    self					:					Optional[int]      )   ->	Optional[Any]:
    _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=20			,						generator=lowerCamelCase__			,						output_type='''np'''			,						)
    _A      =       output.images
    _A      =       images[0, 2_55:2_58, 2_55:2_58, -1]

    assert images.shape == (1, 5_12, 5_12, 3)
    _A      =       np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]      )

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






def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Any			 =[int(item) for item in user_input.split(""",""")]
					print(stooge_sort(unsorted))