"""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_   (  __UpperCamelCase			,		unittest.TestCase      ):
					# TODO: is there an appropriate internal test set?
					UpperCamelCase_			:						Union[str, Any]      =     "ssube/stable-diffusion-x4-upscaler-onnx"




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  Tuple=0  ):
							lowerCAmelCase__   = floats_tensor((1, 3, 128, 128)	,    rng=random.Random(snake_case__  )  )
							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = {
							    """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     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider="""CPUExecutionProvider"""  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							# started as 128, should now be 512
							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]  )
							assert np.abs(image_slice - expected_slice  ).max() < 1E-1




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider="""CPUExecutionProvider"""  )
							lowerCAmelCase__   = PNDMScheduler.from_config(pipe.scheduler.config	,    skip_prk_steps=snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]  )
							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider="""CPUExecutionProvider"""  )
							lowerCAmelCase__   = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider="""CPUExecutionProvider"""  )
							lowerCAmelCase__   = EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]  )
							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider="""CPUExecutionProvider"""  )
							lowerCAmelCase__   = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]  )

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

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




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = ort.SessionOptions()
							lowerCAmelCase__   = False
							return options




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

							lowerCAmelCase__   = """A fantasy landscape, trending on artstation"""

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

							assert images.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]  )
							# 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    (						self    :  int  ):
							lowerCAmelCase__   = load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg"""  )
							lowerCAmelCase__   = init_image.resize((128, 128)  )
							lowerCAmelCase__   = LMSDiscreteScheduler.from_pretrained(
							    """ssube/stable-diffusion-x4-upscaler-onnx"""	,    subfolder="""scheduler"""  )
							lowerCAmelCase__   = OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    """ssube/stable-diffusion-x4-upscaler-onnx"""	,    scheduler=snake_case__	,    provider=self.gpu_provider	,    sess_options=self.gpu_options	,    )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """A fantasy landscape, trending on artstation"""

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

							assert images.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array(
							    [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]  )
							# 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		(							lowerCamelCase__  ):





		"""simple docstring"""




		if len(lowerCamelCase__  ) <= 1:
				return lst

		lowerCAmelCase__   = 1

		while i < len(lowerCamelCase__  ):
				if lst[i - 1] <= lst[i]:
						i += 1
				else:
						lowerCAmelCase__ ,       lowerCAmelCase__   = lst[i], lst[i - 1]
						i -= 1
						if i == 0:
								lowerCAmelCase__   = 1

		return lst


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




