




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   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''ssube/stable-diffusion-x4-upscaler-onnx'''

 def 				a__ (				self					:  Tuple						,							A_					:  str=0  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    floats_tensor((1, 3, 128, 128)						,							rng=random.Random(A_  )  )
       lowerCamelCase_		     =    torch.manual_seed(A_  )
       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 				a__ (				self					:  int  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider='CPUExecutionProvider'  )
       pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_		     =    self.get_dummy_inputs()
       lowerCamelCase_		     =    pipe(**A_  ).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(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]  )
       assert np.abs(image_slice - expected_slice  ).max() < 1E-1

 def 				a__ (				self					:  Tuple  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider='CPUExecutionProvider'  )
       lowerCamelCase_		     =    PNDMScheduler.from_config(pipe.scheduler.config						,							skip_prk_steps=A_  )
       pipe.set_progress_bar_config(disable=A_  )

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    np.array(
           [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]  )
       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1

 def 				a__ (				self					:  Any  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider='CPUExecutionProvider'  )
       lowerCamelCase_		     =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=A_  )

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    np.array(
           [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]  )

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

 def 				a__ (				self					:  Union[str, Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider='CPUExecutionProvider'  )
       lowerCamelCase_		     =    EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=A_  )

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]  )
       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1





 def 				a__ (				self					:  str  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider='CPUExecutionProvider'  )
       lowerCamelCase_		     =    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=A_  )

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    np.array(
           [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]  )

       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					:  int  )   ->	Optional[int]:

       """simple docstring"""






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )

 @property
 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    ort.SessionOptions()
       lowerCamelCase_		     =    False
       return options

 def 				a__ (				self					:  Tuple  )   ->	Optional[Any]:

       """simple docstring"""






       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=A_  )

       lowerCamelCase_		     =    'A fantasy landscape, trending on artstation'

       lowerCamelCase_		     =    torch.manual_seed(0  )
       lowerCamelCase_		     =    pipe(
           prompt=A_						,							image=A_						,							guidance_scale=7.5						,							num_inference_steps=10						,							generator=A_						,							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 				a__ (				self					:  str  )   ->	List[str]:

       """simple docstring"""






       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=A_						,							provider=self.gpu_provider						,							sess_options=self.gpu_options						,							)
       pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_		     =    'A fantasy landscape, trending on artstation'

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

       assert images.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    np.array(
           [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]  )
       # 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			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



