




'''simple docstring'''






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 					A		( snake_case_			,							unittest.TestCase		):




				'''simple docstring'''





				A      	=				'''ssube/stable-diffusion-x4-upscaler-onnx'''
				def        a_       (self ,  _UpperCAmelCase=0			)	->       Tuple:
							__UpperCamelCase      :			Union[str, Any]									=   floats_tensor((1, 3, 1_2_8, 1_2_8) ,  rng=random.Random(UpperCamelCase__			)			)
							__UpperCamelCase      :			Any									=   torch.manual_seed(UpperCamelCase__			)
							__UpperCamelCase      :			List[str]									=   {
							    "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        a_       (self			)	->       Tuple:
							__UpperCamelCase      :			Optional[Any]									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,  provider="CPUExecutionProvider"			)
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Tuple									=   self.get_dummy_inputs()
							__UpperCamelCase      :			int									=   pipe(**UpperCamelCase__			).images
							__UpperCamelCase      :			Tuple									=   image[0, -3:, -3:, -1].flatten()

							# started as 128, should now be 512
							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			Optional[int]									=   np.array(
							    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]			)
							assert np.abs(image_slice - expected_slice			).max() < 1E-1
				def        a_       (self			)	->       Union[str, Any]:
							__UpperCamelCase      :			Tuple									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,  provider="CPUExecutionProvider"			)
							__UpperCamelCase      :			Optional[Any]									=   PNDMScheduler.from_config(pipe.scheduler.config ,  skip_prk_steps=UpperCamelCase__			)
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Tuple									=   self.get_dummy_inputs()
							__UpperCamelCase      :			Optional[Any]									=   pipe(**UpperCamelCase__			).images
							__UpperCamelCase      :			List[str]									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			Dict									=   np.array(
							    [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]			)
							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1
				def        a_       (self			)	->       Any:
							__UpperCamelCase      :			Optional[int]									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,  provider="CPUExecutionProvider"			)
							__UpperCamelCase      :			Optional[Any]									=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Any									=   self.get_dummy_inputs()
							__UpperCamelCase      :			List[Any]									=   pipe(**UpperCamelCase__			).images
							__UpperCamelCase      :			List[str]									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			Optional[Any]									=   np.array(
							    [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1
				def        a_       (self			)	->       Any:
							__UpperCamelCase      :			Union[str, Any]									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,  provider="CPUExecutionProvider"			)
							__UpperCamelCase      :			Any									=   EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Dict									=   self.get_dummy_inputs()
							__UpperCamelCase      :			Union[str, Any]									=   pipe(**UpperCamelCase__			).images
							__UpperCamelCase      :			str									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			Union[str, Any]									=   np.array(
							    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]			)
							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1





				def        a_       (self			)	->       str:
							__UpperCamelCase      :			Optional[Any]									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,  provider="CPUExecutionProvider"			)
							__UpperCamelCase      :			Optional[Any]									=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Any									=   self.get_dummy_inputs()
							__UpperCamelCase      :			int									=   pipe(**UpperCamelCase__			).images
							__UpperCamelCase      :			List[Any]									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			Union[str, Any]									=   np.array(
							    [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]			)

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







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




				'''simple docstring'''





				@property
				def        a_       (self			)	->       Tuple:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)
				@property
				def        a_       (self			)	->       List[Any]:
							__UpperCamelCase      :			List[Any]									=   ort.SessionOptions()
							__UpperCamelCase      :			str									=   False
							return options
				def        a_       (self			)	->       List[Any]:
							__UpperCamelCase      :			str									=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"			)
							__UpperCamelCase      :			Tuple									=   init_image.resize((1_2_8, 1_2_8)			)
							# using the PNDM scheduler by default
							__UpperCamelCase      :			Tuple									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx" ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			List[Any]									=   "A fantasy landscape, trending on artstation"

							__UpperCamelCase      :			Union[str, Any]									=   torch.manual_seed(0			)
							__UpperCamelCase      :			int									=   pipe(
							    prompt=UpperCamelCase__ ,  image=UpperCamelCase__ ,  guidance_scale=7.5 ,  num_inference_steps=1_0 ,  generator=UpperCamelCase__ ,  output_type="np" ,  )
							__UpperCamelCase      :			str									=   output.images
							__UpperCamelCase      :			Tuple									=   images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

							assert images.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			int									=   np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]			)
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





				def        a_       (self			)	->       Optional[Any]:
							__UpperCamelCase      :			Tuple									=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"			)
							__UpperCamelCase      :			Optional[Any]									=   init_image.resize((1_2_8, 1_2_8)			)
							__UpperCamelCase      :			Union[str, Any]									=   LMSDiscreteScheduler.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx" ,  subfolder="scheduler"			)
							__UpperCamelCase      :			List[Any]									=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx" ,  scheduler=UpperCamelCase__ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
							pipe.set_progress_bar_config(disable=UpperCamelCase__			)

							__UpperCamelCase      :			Union[str, Any]									=   "A fantasy landscape, trending on artstation"

							__UpperCamelCase      :			Tuple									=   torch.manual_seed(0			)
							__UpperCamelCase      :			str									=   pipe(
							    prompt=UpperCamelCase__ ,  image=UpperCamelCase__ ,  guidance_scale=7.5 ,  num_inference_steps=2_0 ,  generator=UpperCamelCase__ ,  output_type="np" ,  )
							__UpperCamelCase      :			List[Any]									=   output.images
							__UpperCamelCase      :			List[Any]									=   images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

							assert images.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase      :			int									=   np.array(
							    [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]			)
							# 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   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


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





