"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))
"""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      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]=0    )    ->       int:
         __snake_case				=							floats_tensor((1, 3, 128, 128)	,      rng=random.Random(A_    )    )
         __snake_case				=							np.random.RandomState(A_    )
         __snake_case				=							{
             '''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  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]    )
         assert np.abs(image_slice - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							PNDMScheduler.from_config(pipe.scheduler.config	,      skip_prk_steps=A_    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       str:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         # warmup pass to apply optimizations
         __snake_case				=							pipe(**self.get_dummy_inputs()    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

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







    def  lowercase					(		self							:     List[str]    )    ->       Any:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]    )

         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  lowercase					(		self							:     str    )    ->       int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							ort.SessionOptions()
         __snake_case				=							False
         return options
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         # using the PNDM scheduler by default
         __snake_case				=							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				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=10	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]    )
         # 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							:     str    )    ->       Optional[int]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         __snake_case				=							LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      subfolder='''scheduler'''	,      revision='''onnx'''    )
         __snake_case				=							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				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=20	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]    )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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