






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"



					def  _lowercase  (      self  :						Tuple							,	__A  :						Union[str, Any]=0 ):
						snake_case__				:   Optional[int]     =						floats_tensor((1, 3, 1_2_8, 1_2_8)							,	rng=random.Random(__A ) )
						snake_case__				:   Union[str, Any]     =						np.random.RandomState(__A )
						snake_case__				:   Dict     =						{
						    "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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[Any]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Tuple     =						self.get_dummy_inputs()
						snake_case__				:   Optional[Any]     =						pipe(**__A ).images
						snake_case__				:   Dict     =						image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   Any     =						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  _lowercase  (      self  :						Any ):
						snake_case__				:   int     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						snake_case__				:   Optional[Any]     =						PNDMScheduler.from_config(pipe.scheduler.config							,	skip_prk_steps=__A )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Union[str, Any]     =						self.get_dummy_inputs()
						snake_case__				:   Optional[int]     =						pipe(**__A ).images
						snake_case__				:   Optional[int]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   Dict     =						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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						snake_case__				:   List[Any]     =						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
						pipe.set_progress_bar_config(disable=__A )

						# warmup pass to apply optimizations
						snake_case__				:   int     =						pipe(**self.get_dummy_inputs() )

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   List[str]     =						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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[Any]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						snake_case__				:   Union[str, Any]     =						EulerDiscreteScheduler.from_config(pipe.scheduler.config )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   int     =						self.get_dummy_inputs()
						snake_case__				:   Dict     =						pipe(**__A ).images
						snake_case__				:   Optional[int]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   List[str]     =						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  _lowercase  (      self  :						int ):
						snake_case__				:   Any     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						snake_case__				:   str     =						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Tuple     =						self.get_dummy_inputs()
						snake_case__				:   int     =						pipe(**__A ).images
						snake_case__				:   Union[str, Any]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   List[Any]     =						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  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,	provider="CPUExecutionProvider" )
						snake_case__				:   Optional[int]     =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Optional[Any]     =						self.get_dummy_inputs()
						snake_case__				:   Optional[Any]     =						pipe(**__A ).images
						snake_case__				:   Optional[Any]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__				:   int     =						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 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@property
					def  _lowercase  (      self  :						Union[str, Any] ):
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)



					@property
					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Any     =						ort.SessionOptions()
						snake_case__				:   Union[str, Any]     =						False
						return options



					def  _lowercase  (      self  :						str ):
						snake_case__				:   int     =						load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg" )
						snake_case__				:   Optional[int]     =						init_image.resize((7_6_8, 5_1_2) )
						# using the PNDM scheduler by default
						snake_case__				:   Union[str, Any]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,	revision="onnx"							,	safety_checker=__A							,	feature_extractor=__A							,	provider=self.gpu_provider							,	sess_options=self.gpu_options							,	)
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Tuple     =						"A fantasy landscape, trending on artstation"

						snake_case__				:   Tuple     =						np.random.RandomState(0 )
						snake_case__				:   str     =						pipe(
						    prompt=__A							,	image=__A							,	strength=0.7_5							,	guidance_scale=7.5							,	num_inference_steps=1_0							,	generator=__A							,	output_type="np"							,	)
						snake_case__				:   str     =						output.images
						snake_case__				:   Any     =						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 7_6_8, 3)
						snake_case__				:   List[Any]     =						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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[Any]     =						load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg" )
						snake_case__				:   int     =						init_image.resize((7_6_8, 5_1_2) )
						snake_case__				:   Dict     =						LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,	subfolder="scheduler"							,	revision="onnx" )
						snake_case__				:   Optional[Any]     =						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,	revision="onnx"							,	scheduler=__A							,	safety_checker=__A							,	feature_extractor=__A							,	provider=self.gpu_provider							,	sess_options=self.gpu_options							,	)
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Optional[int]     =						"A fantasy landscape, trending on artstation"

						snake_case__				:   Tuple     =						np.random.RandomState(0 )
						snake_case__				:   Any     =						pipe(
						    prompt=__A							,	image=__A							,	strength=0.7_5							,	guidance_scale=7.5							,	num_inference_steps=2_0							,	generator=__A							,	output_type="np"							,	)
						snake_case__				:   List[Any]     =						output.images
						snake_case__				:   List[Any]     =						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 7_6_8, 3)
						snake_case__				:   Dict     =						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











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							list     ):
	if len(snake_case_     ) <= 1:
		return lst

	snake_case__				:   List[Any]     =						1

	while i < len(snake_case_     ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			snake_case__,		snake_case__				:   Tuple     =						lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				snake_case__				:   Union[str, Any]     =						1

	return lst


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



