


import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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   ):
			# TODO: is there an appropriate internal test set?
			__snake_case    :				Tuple									=							"ssube/stable-diffusion-x4-upscaler-onnx"







			def 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  Dict=0					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  floats_tensor((1, 3, 128, 128)				,     rng=random.Random(_A					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(_A					)
									_SCREAMING_SNAKE_CASE     				=  {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,     provider="""CPUExecutionProvider"""					)
									pipe.set_progress_bar_config(disable=_A					)

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

									# started as 128, should now be 512
									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]					)
									assert np.abs(image_slice - expected_slice					).max() < 1E-1







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,     provider="""CPUExecutionProvider"""					)
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=_A					)
									pipe.set_progress_bar_config(disable=_A					)

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

									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]					)
									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,     provider="""CPUExecutionProvider"""					)
									_SCREAMING_SNAKE_CASE     				=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
									pipe.set_progress_bar_config(disable=_A					)

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

									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]					)

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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,     provider="""CPUExecutionProvider"""					)
									_SCREAMING_SNAKE_CASE     				=  EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
									pipe.set_progress_bar_config(disable=_A					)

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

									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]					)
									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,     provider="""CPUExecutionProvider"""					)
									_SCREAMING_SNAKE_CASE     				=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
									pipe.set_progress_bar_config(disable=_A					)

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

									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]					)

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



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







			@property
			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)







			@property
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  ort.SessionOptions()
									_SCREAMING_SNAKE_CASE     				=  False
									return options







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""					)
									_SCREAMING_SNAKE_CASE     				=  init_image.resize((128, 128)					)
									# using the PNDM scheduler by default
									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
									    """ssube/stable-diffusion-x4-upscaler-onnx"""				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
									pipe.set_progress_bar_config(disable=_A					)

									_SCREAMING_SNAKE_CASE     				=  'A fantasy landscape, trending on artstation'

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(
									    prompt=_A				,     image=_A				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=_A				,     output_type="""np"""				,     )
									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]					)
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""					)
									_SCREAMING_SNAKE_CASE     				=  init_image.resize((128, 128)					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_pretrained(
									    """ssube/stable-diffusion-x4-upscaler-onnx"""				,     subfolder="""scheduler"""					)
									_SCREAMING_SNAKE_CASE     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
									    """ssube/stable-diffusion-x4-upscaler-onnx"""				,     scheduler=_A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
									pipe.set_progress_bar_config(disable=_A					)

									_SCREAMING_SNAKE_CASE     				=  'A fantasy landscape, trending on artstation'

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(
									    prompt=_A				,     image=_A				,     guidance_scale=7.5				,     num_inference_steps=20				,     generator=_A				,     output_type="""np"""				,     )
									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]					)
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		)
						for i in range(length - 1		):
												_SCREAMING_SNAKE_CASE     				=  i
												for k in range(i + 1	,snake_case__		):
																		if collection[k] < collection[least]:
																								_SCREAMING_SNAKE_CASE     				=  k
												if least != i:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (collection[i], collection[least])
						return collection


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

