






"""simple docstring"""



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    lowercase__			(							snake_case__,   unittest.TestCase   ):
			_UpperCAmelCase		:Dict   			= '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Optional[Any]=0					):
										lowerCamelCase_       :							Optional[int]									=floats_tensor((1, 3, 128, 128)   ,       rng=random.Random(SCREAMING_SNAKE_CASE__					)					)
										lowerCamelCase_       :							int									=np.random.RandomState(SCREAMING_SNAKE_CASE__					)
										lowerCamelCase_       :							Dict									={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''strength''': 0.75,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							Any									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

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

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							Optional[Any]									=np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]					)
										assert np.abs(image_slice - expected_slice					).max() < 1E-1


			def 	UpperCAmelCase__						(      self     :  int					):
										lowerCamelCase_       :							int									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										lowerCamelCase_       :							Union[str, Any]									=PNDMScheduler.from_config(pipe.scheduler.config   ,       skip_prk_steps=SCREAMING_SNAKE_CASE__					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										lowerCamelCase_       :							int									=self.get_dummy_inputs()
										lowerCamelCase_       :							Optional[int]									=pipe(**SCREAMING_SNAKE_CASE__					).images
										lowerCamelCase_       :							Tuple									=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							Tuple									=np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]					)

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


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							Optional[Any]									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										lowerCamelCase_       :							List[Any]									=LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										# warmup pass to apply optimizations
										lowerCamelCase_       :							List[str]									=pipe(**self.get_dummy_inputs()					)

										lowerCamelCase_       :							Dict									=self.get_dummy_inputs()
										lowerCamelCase_       :							List[Any]									=pipe(**SCREAMING_SNAKE_CASE__					).images
										lowerCamelCase_       :							List[Any]									=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							str									=np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]					)

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


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Optional[Any]									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										lowerCamelCase_       :							str									=EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

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

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							Tuple									=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

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


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Union[str, Any]									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										lowerCamelCase_       :							Optional[Any]									=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										lowerCamelCase_       :							str									=self.get_dummy_inputs()
										lowerCamelCase_       :							Tuple									=pipe(**SCREAMING_SNAKE_CASE__					).images
										lowerCamelCase_       :							Dict									=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							List[Any]									=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

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


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							List[str]									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,       provider="CPUExecutionProvider"					)
										lowerCamelCase_       :							Dict									=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										lowerCamelCase_       :							List[str]									=self.get_dummy_inputs()
										lowerCamelCase_       :							str									=pipe(**SCREAMING_SNAKE_CASE__					).images
										lowerCamelCase_       :							Optional[int]									=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowerCamelCase_       :							Optional[Any]									=np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]					)

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







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


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


			@property
			def 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_       :							List[str]									=ort.SessionOptions()
										lowerCamelCase_       :							int									=False
										return options


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							str									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"					)
										lowerCamelCase_       :							int									=init_image.resize((768, 512)					)
										# using the PNDM scheduler by default
										lowerCamelCase_       :							str									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"   ,       revision="onnx"   ,       safety_checker=SCREAMING_SNAKE_CASE__   ,       feature_extractor=SCREAMING_SNAKE_CASE__   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										lowerCamelCase_       :							Optional[Any]									='''A fantasy landscape, trending on artstation'''

										lowerCamelCase_       :							int									=np.random.RandomState(0					)
										lowerCamelCase_       :							Optional[int]									=pipe(
										    prompt=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       strength=0.75   ,       guidance_scale=7.5   ,       num_inference_steps=10   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="np"   ,       )
										lowerCamelCase_       :							Any									=output.images
										lowerCamelCase_       :							List[Any]									=images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										lowerCamelCase_       :							Optional[int]									=np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]					)
										# 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     :  Tuple					):
										lowerCamelCase_       :							Tuple									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"					)
										lowerCamelCase_       :							Tuple									=init_image.resize((768, 512)					)
										lowerCamelCase_       :							Optional[Any]									=LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"   ,       subfolder="scheduler"   ,       revision="onnx"					)
										lowerCamelCase_       :							Optional[Any]									=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"   ,       revision="onnx"   ,       scheduler=SCREAMING_SNAKE_CASE__   ,       safety_checker=SCREAMING_SNAKE_CASE__   ,       feature_extractor=SCREAMING_SNAKE_CASE__   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

										lowerCamelCase_       :							Dict									='''A fantasy landscape, trending on artstation'''

										lowerCamelCase_       :							List[str]									=np.random.RandomState(0					)
										lowerCamelCase_       :							Optional[int]									=pipe(
										    prompt=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       strength=0.75   ,       guidance_scale=7.5   ,       num_inference_steps=20   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="np"   ,       )
										lowerCamelCase_       :							Dict									=output.images
										lowerCamelCase_       :							List[str]									=images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										lowerCamelCase_       :							Optional[int]									=np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]					)
										# 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   (							a_     :		list							)			->      list:

      if len(a_							) < 2:
            return collection

      def circle_sort_util(a_     :		list ,   a_     :		int ,   a_     :		int							) -> bool:
            __SCREAMING_SNAKE_CASE   :int						=  False

            if low == high:
                  return swapped

            __SCREAMING_SNAKE_CASE   :int						=  low
            __SCREAMING_SNAKE_CASE   :Tuple						=  high

            while left < right:
                  if collection[left] > collection[right]:
                        __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  (
                            collection[right],
                            collection[left],
                        )
                        __SCREAMING_SNAKE_CASE   :Optional[int]						=  True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  (
                      collection[right + 1],
                      collection[left],
                  )

                  __SCREAMING_SNAKE_CASE   :Tuple						=  True

            __SCREAMING_SNAKE_CASE   :int						=  low + int((high - low) / 2							)
            __SCREAMING_SNAKE_CASE   :int						=  circle_sort_util(a_ ,   a_ ,   a_							)
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  circle_sort_util(a_ ,   mid + 1 ,   a_							)

            return swapped or left_swap or right_swap

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  True

      while is_not_sorted is True:
            __SCREAMING_SNAKE_CASE   :Tuple						=  circle_sort_util(a_ ,   0 ,   len(a_							) - 1							)

      return collection


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