


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    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[Any]							=					"ssube/stable-diffusion-x4-upscaler-onnx"






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE=0						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		floats_tensor((1, 3, 128, 128) ,					rng=random.Random(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Dict   	=		torch.manual_seed(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		{
                  '''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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,					provider='''CPUExecutionProvider'''						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

              # started as 128, should now be 512
              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Union[str, Any]   	=		np.array(
                  [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
              assert np.abs(image_slice - expected_slice						).max() < 1e-1






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,					provider='''CPUExecutionProvider'''						)
              UpperCamelCase    :	List[str]   	=		PNDMScheduler.from_config(pipe.scheduler.config ,					skip_prk_steps=__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	List[str]   	=		np.array(
                  [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]						)
              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,					provider='''CPUExecutionProvider'''						)
              UpperCamelCase    :	List[Any]   	=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	int   	=		np.array(
                  [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]						)

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,					provider='''CPUExecutionProvider'''						)
              UpperCamelCase    :	List[Any]   	=		EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	List[Any]   	=		np.array(
                  [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,					provider='''CPUExecutionProvider'''						)
              UpperCamelCase    :	List[str]   	=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	int   	=		np.array(
                  [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]						)

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




@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




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






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		ort.SessionOptions()
              UpperCamelCase    :	Optional[Any]   	=		False
              return options






       def  _lowercase   (     self						):




              """simple docstring"""




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

              UpperCamelCase    :	Tuple   	=		'''A fantasy landscape, trending on artstation'''

              UpperCamelCase    :	Any   	=		torch.manual_seed(0						)
              UpperCamelCase    :	str   	=		pipe(
                  prompt=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=10 ,					generator=__SCREAMING_SNAKE_CASE ,					output_type='''np''' ,					)
              UpperCamelCase    :	List[Any]   	=		output.images
              UpperCamelCase    :	List[Any]   	=		images[0, 255:258, 383:386, -1]

              assert images.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Any   	=		np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]						)
              # 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						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/img2img/sketch-mountains-input.jpg'''						)
              UpperCamelCase    :	int   	=		init_image.resize((128, 128)						)
              UpperCamelCase    :	Dict   	=		LMSDiscreteScheduler.from_pretrained(
                  '''ssube/stable-diffusion-x4-upscaler-onnx''' ,					subfolder='''scheduler'''						)
              UpperCamelCase    :	List[str]   	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
                  '''ssube/stable-diffusion-x4-upscaler-onnx''' ,					scheduler=__SCREAMING_SNAKE_CASE ,					provider=self.gpu_provider ,					sess_options=self.gpu_options ,					)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		'''A fantasy landscape, trending on artstation'''

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

              assert images.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Union[str, Any]   	=		np.array(
                  [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]						)
              # 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	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if a < 0 or b < 0:
              raise ValueError('''the value of both inputs must be positive'''       )
       UpperCamelCase    :	int   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]  # remove the leading "0b"
       UpperCamelCase    :	List[str]   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]
       UpperCamelCase    :	Tuple   	=		max(len(SCREAMING_SNAKE_CASE_       )							,			len(SCREAMING_SNAKE_CASE_       )       )
       return "0b" + "".join(
           str(int('''1''' in (char_a, char_b)       )       )
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_       )							,			b_binary.zfill(SCREAMING_SNAKE_CASE_       )       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

