





"""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 					_SCREAMING_SNAKE_CASE(   A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Dict							=  '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  floats_tensor((1, 3, 1_28, 1_28)		,rng=random.Random(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE   :int						=  np.random.RandomState(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  {
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''strength''': 0.7_5,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,provider='''CPUExecutionProvider'''							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]							)
             assert np.abs(image_slice - expected_slice							).max() < 1E-1




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





             """simple docstring"""






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

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :Tuple						=  np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]							)

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




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





             """simple docstring"""






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

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

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :str						=  np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]							)

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




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






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

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :Tuple						=  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]							)

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




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





             """simple docstring"""






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

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]							)

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




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





             """simple docstring"""






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

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

             assert image.shape == (1, 1_28, 1_28, 3)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]							)

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






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




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  ort.SessionOptions()
             __SCREAMING_SNAKE_CASE   :int						=  False
             return options




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/img2img/sketch-mountains-input.jpg'''							)
             __SCREAMING_SNAKE_CASE   :int						=  init_image.resize((7_68, 5_12)							)
             # using the PNDM scheduler by default
             __SCREAMING_SNAKE_CASE   :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__							)

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

             __SCREAMING_SNAKE_CASE   :int						=  np.random.RandomState(0							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__		,image=SCREAMING_SNAKE_CASE__		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=10		,generator=SCREAMING_SNAKE_CASE__		,output_type='''np'''		,)
             __SCREAMING_SNAKE_CASE   :Any						=  output.images
             __SCREAMING_SNAKE_CASE   :List[Any]						=  images[0, 2_55:2_58, 3_83:3_86, -1]

             assert images.shape == (1, 5_12, 7_68, 3)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]							)
             # 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							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/img2img/sketch-mountains-input.jpg'''							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  init_image.resize((7_68, 5_12)							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  LMSDiscreteScheduler.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''		,subfolder='''scheduler'''		,revision='''onnx'''							)
             __SCREAMING_SNAKE_CASE   :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__							)

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

             __SCREAMING_SNAKE_CASE   :List[str]						=  np.random.RandomState(0							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__		,image=SCREAMING_SNAKE_CASE__		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=20		,generator=SCREAMING_SNAKE_CASE__		,output_type='''np'''		,)
             __SCREAMING_SNAKE_CASE   :Dict						=  output.images
             __SCREAMING_SNAKE_CASE   :List[str]						=  images[0, 2_55:2_58, 3_83:3_86, -1]

             assert images.shape == (1, 5_12, 7_68, 3)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]							)
             # 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_     :		int							)			->      bool:
      if not isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :int						=  f'''Input value of [number={number}] must be an integer'''
            raise TypeError(a_							)
      if number < 0:
            return False
      __SCREAMING_SNAKE_CASE   :Any						=  number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()