

'''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__   (	A__					,       unittest.TestCase  ):
  A__					=    'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





  def 						A  (		self				:     Optional[Any]							, _a				:     int=0			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =floats_tensor((1, 3, 128, 128)							, rng=random.Random(_a			)			)
       _SCREAMING_SNAKE_CASE							   =np.random.RandomState(_a			)
       _SCREAMING_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 						A  (		self				:     Dict			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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 						A  (		self				:     str			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =PNDMScheduler.from_config(pipe.scheduler.config							, skip_prk_steps=_a			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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 						A  (		self				:     List[Any]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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 						A  (		self				:     List[Any]			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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 						A  (		self				:     Any			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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 						A  (		self				:     Any			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_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  ):





  @property
  def 						A  (		self				:     List[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Any			)     ->   Dict:





       '''simple docstring'''






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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'			)
       _SCREAMING_SNAKE_CASE							   =init_image.resize((768, 512)			)
       # using the PNDM scheduler by default
       _SCREAMING_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			)

       _SCREAMING_SNAKE_CASE							   ='A fantasy landscape, trending on artstation'

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

       assert images.shape == (1, 512, 768, 3)
       _SCREAMING_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 						A  (		self				:     Union[str, Any]			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'			)
       _SCREAMING_SNAKE_CASE							   =init_image.resize((768, 512)			)
       _SCREAMING_SNAKE_CASE							   =LMSDiscreteScheduler.from_pretrained(
           'runwayml/stable-diffusion-v1-5'							, subfolder='scheduler'							, revision='onnx'			)
       _SCREAMING_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			)

       _SCREAMING_SNAKE_CASE							   ='A fantasy landscape, trending on artstation'

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

       assert images.shape == (1, 512, 768, 3)
       _SCREAMING_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



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'    )
     return 0.5 * mass * abs(_UpperCamelCase    ) * abs(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
