import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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__(			_snake_case		,						unittest.TestCase ):
						lowerCAmelCase__  :						Tuple													=		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





						def 				snake_case__	(       self		,__UpperCAmelCase=0     )					->				Dict:
													A__	     =							floats_tensor((1, 3, 1_28, 1_28)		,rng=random.Random(lowerCAmelCase__     )     )
													A__	     =							np.random.RandomState(lowerCAmelCase__     )
													A__	     =							{
													    'prompt': 'A painting of a squirrel eating a burger',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'strength': 0.7_5,
													    'guidance_scale': 7.5,
													    'output_type': 'numpy',
													}
													return inputs





						def 				snake_case__	(       self     )					->				int:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]     )
													assert np.abs(image_slice - expected_slice     ).max() < 1e-1





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													A__	     =							PNDMScheduler.from_config(pipe.scheduler.config		,skip_prk_steps=lowerCAmelCase__     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1]

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]     )

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





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													A__	     =							LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													# warmup pass to apply optimizations
													A__	     =							pipe(**self.get_dummy_inputs()     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1]

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]     )

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





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													A__	     =							EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1]

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]     )

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





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													A__	     =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1]

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]     )

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





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='CPUExecutionProvider'     )
													A__	     =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

													A__	     =							self.get_dummy_inputs()
													A__	     =							pipe(**lowerCAmelCase__     ).images
													A__	     =							image[0, -3:, -3:, -1]

													assert image.shape == (1, 1_28, 1_28, 3)
													A__	     =							np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]     )

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






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





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





						@property
						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'     )
													A__	     =							init_image.resize((7_68, 5_12)     )
													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,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 fantasy landscape, trending on artstation'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=lowerCAmelCase__		,image=lowerCAmelCase__		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=10		,generator=lowerCAmelCase__		,output_type='np'		,)
													A__	     =							output.images
													A__	     =							images[0, 2_55:2_58, 3_83:3_86, -1]

													assert images.shape == (1, 5_12, 7_68, 3)
													A__	     =							np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]     )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2e-2





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'     )
													A__	     =							init_image.resize((7_68, 5_12)     )
													A__	     =							LMSDiscreteScheduler.from_pretrained(
													    'runwayml/stable-diffusion-v1-5'		,subfolder='scheduler'		,revision='onnx'     )
													A__	     =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
													    'runwayml/stable-diffusion-v1-5'		,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 fantasy landscape, trending on artstation'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=lowerCAmelCase__		,image=lowerCAmelCase__		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=20		,generator=lowerCAmelCase__		,output_type='np'		,)
													A__	     =							output.images
													A__	     =							images[0, 2_55:2_58, 3_83:3_86, -1]

													assert images.shape == (1, 5_12, 7_68, 3)
													A__	     =							np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]     )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2e-2






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


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





